1 Introduction
I want to use this document to examine our first round of persistence samples. I checked my email from Najib and did not find a sample sheet but did find an explanation of the three sample types we expect.
In preparation for this, I downloaded a new hg38 genome. Since the panamensis asembly has not significantly changed (excepting the putative long read genome which I have not yet seen), I am just using the same one.
2 Loading annotation
The hg38 genome I got is from 202405. The various ensembl archive servers have been having troubles recently, so I may need to cherry-pick one which is working. I will therefore also load annotations from org.He.eg.db in case ensembl is not responding.
## Ok, so useast.ensembl is failing today, let us use the jan2024 archive?
#hs_annot <- load_biomart_annotations(archive = FALSE, species = "hsapiens")
## Seems like the 202401 archive is a good choice, it is explicitly the hg38_111 release.
## and it is waaaaay faster (like 100x) than useast right now.
## Note, I am explicitly not using the archive in the following invocation because none of them were
## responding, so that was a whole big annoying thing.
hs_annot <- load_biomart_annotations(archive = FALSE, species = "hsapiens")## The biomart annotations file already exists, loading from it.hs_orgdb <- load_orgdb_annotations("org.Hs.eg.db", fields = c("SYMBOL", "ENSEMBL", "ENTREZID"))[["genes"]]## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:base':
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## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff, setequal, union##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:hpgltools':
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## conditions, conditions<-, IQR, mad, sd, var## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
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## anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter,
## Find, get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique, unsplit, which.max, which.min## Loading required package: Biobase## Welcome to Bioconductor
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## Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")',
## and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following objects are masked from 'package:hpgltools':
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## exprs<-, notes, pData<-, sampleNames<-## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:utils':
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## findMatches## The following objects are masked from 'package:base':
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## expand.grid, I, unname## ## Unable to find CDSNAME, setting it to GENENAME.## Unable to find GENE_TYPE, setting it to GENETYPE.## Unable to find CDSCHROM in the db, removing it.## Unable to find CDSSTRAND in the db, removing it.## Unable to find CDSSTART in the db, removing it.## Unable to find CDSEND in the db, removing it.## Extracted all gene ids.## Attempting to select: GENENAME, GENETYPE, SYMBOL, ENSEMBL, ENTREZID## 'select()' returned 1:many mapping between keys and columnspanamensis_orgdb_idx <- grep(pattern = "^org.+panamen.+MHOM.+db$", x = rownames(installed.packages()))
panamensis_orgdb <- tail(rownames(installed.packages())[panamensis_orgdb_idx], n = 1)
lp_annot <- load_orgdb_annotations(panamensis_orgdb, keytype = "gid")## ## Unable to find CDSNAME, setting it to ANNOT_EXTERNAL_DB_NAME.## Unable to find CDSCHROM in the db, removing it.## Unable to find CDSSTRAND in the db, removing it.## Unable to find CDSSTART in the db, removing it.## Unable to find CDSEND in the db, removing it.## Extracted all gene ids.## Attempting to select: ANNOT_EXTERNAL_DB_NAME, GENE_TYPE## 'select()' returned 1:1 mapping between keys and columns## all_columns <- keytypes(org.Lmajor.Friedlin.v68.eg.db)
wanted_idx <- grepl(pattern = "^ANNOT", x = all_columns)
wanted_columns <- all_columns[wanted_idx]
gene_ids <- keys(org.Lmajor.Friedlin.v68.eg.db)
major_annotations <- load_orgdb_annotations(major_db, keytype = "gid", fields = wanted_columns)## Unable to find CDSNAME, setting it to ANNOT_EXTERNAL_DB_NAME.## Unable to find CDSCHROM in the db, removing it.## Unable to find CDSSTRAND in the db, removing it.## Unable to find CDSSTART in the db, removing it.## Unable to find CDSEND in the db, removing it.## Extracted all gene ids.## Attempting to select: ANNOT_EXTERNAL_DB_NAME, GENE_TYPE, ANNOT_AA_SEQUENCE_ID, ANNOT_ANNOTATED_GO_COMPONENT, ANNOT_ANNOTATED_GO_FUNCTION, ANNOT_ANNOTATED_GO_ID_COMPONENT, ANNOT_ANNOTATED_GO_ID_FUNCTION, ANNOT_ANNOTATED_GO_ID_PROCESS, ANNOT_ANNOTATED_GO_PROCESS, ANNOT_ANTICODON, ANNOT_APOLLO_LINK_OUT, ANNOT_APOLLO_TRANSCRIPT_DESCRIPTION, ANNOT_CDS, ANNOT_CDS_LENGTH, ANNOT_CHROMOSOME, ANNOT_CODING_END, ANNOT_CODING_START, ANNOT_EC_NUMBERS, ANNOT_EC_NUMBERS_DERIVED, ANNOT_END_MAX, ANNOT_EXON_COUNT, ANNOT_EXTERNAL_DB_NAME, ANNOT_EXTERNAL_DB_VERSION, ANNOT_FIVE_PRIME_UTR_LENGTH, ANNOT_GENE_CONTEXT_END, ANNOT_GENE_CONTEXT_START, ANNOT_GENE_END_MAX, ANNOT_GENE_END_MAX_TEXT, ANNOT_GENE_ENTREZ_ID, ANNOT_GENE_ENTREZ_LINK_DISPLAYTEXT, ANNOT_GENE_ENTREZ_LINK_URL, ANNOT_GENE_EXON_COUNT, ANNOT_GENE_HTS_NONCODING_SNPS, ANNOT_GENE_HTS_NONSYN_SYN_RATIO, ANNOT_GENE_HTS_NONSYNONYMOUS_SNPS, ANNOT_GENE_HTS_STOP_CODON_SNPS, ANNOT_GENE_HTS_SYNONYMOUS_SNPS, ANNOT_GENE_LOCATION_TEXT, ANNOT_GENE_NAME, ANNOT_GENE_ORTHOLOG_NUMBER, ANNOT_GENE_ORTHOMCL_NAME, ANNOT_GENE_PARALOG_NUMBER, ANNOT_GENE_PREVIOUS_IDS, ANNOT_GENE_PRODUCT, ANNOT_GENE_START_MIN, ANNOT_GENE_START_MIN_TEXT, ANNOT_GENE_TOTAL_HTS_SNPS, ANNOT_GENE_TRANSCRIPT_COUNT, ANNOT_GENE_TYPE, ANNOT_GENOMIC_SEQUENCE_LENGTH, ANNOT_GENUS_SPECIES, ANNOT_HAS_MISSING_TRANSCRIPTS, ANNOT_INTERPRO_DESCRIPTION, ANNOT_INTERPRO_ID, ANNOT_IS_DEPRECATED, ANNOT_IS_PSEUDO, ANNOT_ISOELECTRIC_POINT, ANNOT_LOCATION_TEXT, ANNOT_MAP_LOCATION, ANNOT_MCMC_LOCATION, ANNOT_MOLECULAR_WEIGHT, ANNOT_NCBI_TAX_ID, ANNOT_ORTHOMCL_LINK, ANNOT_OVERVIEW, ANNOT_PFAM_DESCRIPTION, ANNOT_PFAM_ID, ANNOT_PIRSF_DESCRIPTION, ANNOT_PIRSF_ID, ANNOT_PREDICTED_GO_COMPONENT, ANNOT_PREDICTED_GO_FUNCTION, ANNOT_PREDICTED_GO_ID_COMPONENT, ANNOT_PREDICTED_GO_ID_FUNCTION, ANNOT_PREDICTED_GO_ID_PROCESS, ANNOT_PREDICTED_GO_PROCESS, ANNOT_PRIMARY_KEY, ANNOT_PROB_MAP, ANNOT_PROB_MCMC, ANNOT_PROSITEPROFILES_DESCRIPTION, ANNOT_PROSITEPROFILES_ID, ANNOT_PROTEIN_LENGTH, ANNOT_PROTEIN_SEQUENCE, ANNOT_PROTEIN_SOURCE_ID, ANNOT_PSEUDO_STRING, ANNOT_SEQUENCE_DATABASE_NAME, ANNOT_SEQUENCE_ID, ANNOT_SIGNALP_PEPTIDE, ANNOT_SMART_DESCRIPTION, ANNOT_SMART_ID, ANNOT_SNPOVERVIEW, ANNOT_SO_ID, ANNOT_SO_TERM_DEFINITION, ANNOT_SO_TERM_NAME, ANNOT_SO_VERSION, ANNOT_START_MIN, ANNOT_STRAND, ANNOT_STRAND_PLUS_MINUS, ANNOT_SUPERFAMILY_DESCRIPTION, ANNOT_SUPERFAMILY_ID, ANNOT_THREE_PRIME_UTR_LENGTH, ANNOT_TIGRFAM_DESCRIPTION, ANNOT_TIGRFAM_ID, ANNOT_TM_COUNT, ANNOT_TRANS_FOUND_PER_GENE_INTERNAL, ANNOT_TRANSCRIPT_INDEX_PER_GENE, ANNOT_TRANSCRIPT_LENGTH, ANNOT_TRANSCRIPT_LINK, ANNOT_TRANSCRIPT_PRODUCT, ANNOT_TRANSCRIPT_SEQUENCE, ANNOT_TRANSCRIPTS_FOUND_PER_GENE, ANNOT_UNIPROT_IDS, ANNOT_UNIPROT_LINKS## 'select()' returned 1:1 mapping between keys and columnsThis is a little silly, but I am going to reload the annotations using the previous invocation to extract the annotation table without having to think. The previous block loads the orgdb for me, so I can just use that to get the fun annotations.
## Unable to find CDSNAME, setting it to ANNOT_EXTERNAL_DB_NAME.## Unable to find CDSCHROM in the db, removing it.## Unable to find CDSSTRAND in the db, removing it.## Unable to find CDSSTART in the db, removing it.## Unable to find CDSEND in the db, removing it.## Extracted all gene ids.## Attempting to select: ANNOT_EXTERNAL_DB_NAME, GENE_TYPE, ANNOT_AA_SEQUENCE_ID, ANNOT_ANNOTATED_GO_COMPONENT, ANNOT_ANNOTATED_GO_FUNCTION, ANNOT_ANNOTATED_GO_ID_COMPONENT, ANNOT_ANNOTATED_GO_ID_FUNCTION, ANNOT_ANNOTATED_GO_ID_PROCESS, ANNOT_ANNOTATED_GO_PROCESS, ANNOT_ANTICODON, ANNOT_APOLLO_LINK_OUT, ANNOT_APOLLO_TRANSCRIPT_DESCRIPTION, ANNOT_CDS, ANNOT_CDS_LENGTH, ANNOT_CHROMOSOME, ANNOT_CODING_END, ANNOT_CODING_START, ANNOT_EC_NUMBERS, ANNOT_EC_NUMBERS_DERIVED, ANNOT_END_MAX, ANNOT_EXON_COUNT, ANNOT_EXTERNAL_DB_NAME, ANNOT_EXTERNAL_DB_VERSION, ANNOT_FIVE_PRIME_UTR_LENGTH, ANNOT_GENE_CONTEXT_END, ANNOT_GENE_CONTEXT_START, ANNOT_GENE_END_MAX, ANNOT_GENE_END_MAX_TEXT, ANNOT_GENE_ENTREZ_ID, ANNOT_GENE_ENTREZ_LINK, ANNOT_GENE_EXON_COUNT, ANNOT_GENE_HTS_NONCODING_SNPS, ANNOT_GENE_HTS_NONSYN_SYN_RATIO, ANNOT_GENE_HTS_NONSYNONYMOUS_SNPS, ANNOT_GENE_HTS_STOP_CODON_SNPS, ANNOT_GENE_HTS_SYNONYMOUS_SNPS, ANNOT_GENE_LOCATION_TEXT, ANNOT_GENE_NAME, ANNOT_GENE_ORTHOLOG_NUMBER, ANNOT_GENE_ORTHOMCL_NAME, ANNOT_GENE_PARALOG_NUMBER, ANNOT_GENE_PREVIOUS_IDS, ANNOT_GENE_PRODUCT, ANNOT_GENE_START_MIN, ANNOT_GENE_START_MIN_TEXT, ANNOT_GENE_TOTAL_HTS_SNPS, ANNOT_GENE_TRANSCRIPT_COUNT, ANNOT_GENE_TYPE, ANNOT_GENOMIC_SEQUENCE_LENGTH, ANNOT_GENUS_SPECIES, ANNOT_HAS_MISSING_TRANSCRIPTS, ANNOT_INTERPRO_DESCRIPTION, ANNOT_INTERPRO_ID, ANNOT_IS_DEPRECATED, ANNOT_IS_PSEUDO, ANNOT_ISOELECTRIC_POINT, ANNOT_LOCATION_TEXT, ANNOT_MAP_LOCATION, ANNOT_MCMC_LOCATION, ANNOT_MOLECULAR_WEIGHT, ANNOT_NCBI_TAX_ID, ANNOT_ORTHOMCL_LINK, ANNOT_OVERVIEW, ANNOT_PFAM_DESCRIPTION, ANNOT_PFAM_ID, ANNOT_PIRSF_DESCRIPTION, ANNOT_PIRSF_ID, ANNOT_PREDICTED_GO_COMPONENT, ANNOT_PREDICTED_GO_FUNCTION, ANNOT_PREDICTED_GO_ID_COMPONENT, ANNOT_PREDICTED_GO_ID_FUNCTION, ANNOT_PREDICTED_GO_ID_PROCESS, ANNOT_PREDICTED_GO_PROCESS, ANNOT_PRIMARY_KEY, ANNOT_PROB_MAP, ANNOT_PROB_MCMC, ANNOT_PROSITEPROFILES_DESCRIPTION, ANNOT_PROSITEPROFILES_ID, ANNOT_PROTEIN_LENGTH, ANNOT_PROTEIN_SEQUENCE, ANNOT_PROTEIN_SOURCE_ID, ANNOT_PSEUDO_STRING, ANNOT_SEQUENCE_DATABASE_NAME, ANNOT_SEQUENCE_ID, ANNOT_SIGNALP_PEPTIDE, ANNOT_SMART_DESCRIPTION, ANNOT_SMART_ID, ANNOT_SNPOVERVIEW, ANNOT_SO_ID, ANNOT_SO_TERM_DEFINITION, ANNOT_SO_TERM_NAME, ANNOT_SO_VERSION, ANNOT_START_MIN, ANNOT_STRAND, ANNOT_STRAND_PLUS_MINUS, ANNOT_SUPERFAMILY_DESCRIPTION, ANNOT_SUPERFAMILY_ID, ANNOT_THREE_PRIME_UTR_LENGTH, ANNOT_TIGRFAM_DESCRIPTION, ANNOT_TIGRFAM_ID, ANNOT_TM_COUNT, ANNOT_TRANS_FOUND_PER_GENE_INTERNAL, ANNOT_TRANSCRIPT_INDEX_PER_GENE, ANNOT_TRANSCRIPT_LENGTH, ANNOT_TRANSCRIPT_LINK, ANNOT_TRANSCRIPT_PRODUCT, ANNOT_TRANSCRIPT_SEQUENCE, ANNOT_TRANSCRIPTS_FOUND_PER_GENE, ANNOT_UNIPROT_IDS, ANNOT_UNIPROT_LINKS## 'select()' returned 1:1 mapping between keys and columns3 Colors
I want a reminder to set the colors for the various states in the data. A fun thing I did recently: I can automagically pull colors from the backgrounds of cells in the sample sheet! Not really relevant right now, but fun.
chosen_colors <- list(
"sample_type" = list(),
"detectionparasiteby7sl" = list(),
"library_type" = list(),
"nasal_7sl_status" = list(),
"any_7sl_status" = list()
"participant_code" = list(),
"detection_type" = list(),
"hs" = list(),
"clinical_presentation" = list(),
"sample_7sl" = list())## Error in parse(text = input): <text>:7:3: unexpected string constant
## 6: "any_7sl_status" = list()
## 7: "participant_code"
## ^4 Collect preprocessed metadata
Use my new sample sheet here. I got the idea recently to make a copy of any freshly downloaded sample sheet and give it the suffix ’_readwrite’ so that I remember to never open the samplesheet by itself in rw.
current_samplesheet <- "sample_sheets/Persistence_v2.1_20261007_readwrite.xlsx"
first_spec <- make_rnaseq_spec()
input <- read_metadata(current_samplesheet)
colnames(input)## [1] "seq" "hpgl_identifier" "aim"
## [4] "participant_code" "sample_type" "tube_label_origin"
## [7] "number_of_vials" "sample_collection_date" "exp_person"
## [10] "clinical_presentation" "parasite" "prior_host_hpgl_code"
## [13] "prior_parasite_hpgl_code" "initial_recurrent" "drug_susceptibility_perc_reduction_gluc"
## [16] "drug_susceptibility_perc_reduction_mlf" "zymodeme_by_electrophoresis" "zymodeme_by_pca"
## [19] "rna_extraction_date" "rna_volume_ul" "rna_qc_date"
## [22] "rna_nanodrop_ng_ul" "X260_280_ratio" "X260_230_ratio"
## [25] "rna_bioanalyzer_or_qubit_ng_ul" "rin" "rna_qc_passed"
## [28] "library_type" "library_version" "library_name"
## [31] "library_const_date" "rna_used_to_construct_libraries_ul" "rna_used_to_construct_libraries_ng"
## [34] "library_qc_date" "lib_qc_passed" "library_volume_ul"
## [37] "unique_dual_index_set" "unique_dual_index_plate_coordinate" "unique_dual_index_id"
## [40] "concentrations_determined_by_1" "primer_conc_ng_ul_30100bp_region_1" "adapter_dimer_conc_ng_ul_100200bp_region_1"
## [43] "library_conc_ng_ul_2001000bp_region_1" "library_molarity_nm_2001000bp_region_1" "library_ave_frag_size_bp_2001000bp_region_1"
## [46] "calculated_adapter_dimer_percent_1" "concentrations_determined_by_2" "primer_conc_ng_ul_30100bp_region_2"
## [49] "adapter_dimer_conc_ng_ul_100200bp_region_2" "library_conc_ng_ul_2001000bp_region_2" "library_molarity_nm_2001000bp_region_2"
## [52] "library_ave_frag_size_bp_2001000bp_region_2" "calculated_adapter_dimer_percent_2" "library_volume_ul_sent_to_umd"
## [55] "shipment_date" "bbiagtc_date_received" "bbiagtc_library_cleanup"
## [58] "bbiagtc_date_sequenced" "bbiagtc_sequence_batch" "bbiagtc_pe_reads_pf"
## [61] "bbiagtc_fastp_duplication_rate" "hisat2inputreads" "lpsingleconaligned"
## [64] "hg38singleconaligned" "lpmulticonaligned" "hg38multiconaligned"
## [67] "lpsingleallaligned" "hg38singleallaligned" "lpmultiallaligned"
## [70] "hg38multiallaligned" "lppercentmappedfromlog" "hg38percentmappedfromlog"
## [73] "lphisatobservedcds" "hg38hisatobservedcds" "krakenmatrix"
## [76] "lphisatgenematrix" "hg38hisatgenematrix" "lpsalmontranscriptmatrix"
## [79] "hg38salmontranscriptmatrix" "detectionparasiteby7sl" "detectionparasiteby18s"
## [82] "detectionparasitebykdnasec" "metabolomicnasalswaborplasma" "immunophenotypingnasalswaborpbmcs"## negative positive NAs
## 153 29 71pre_meta <- gather_preprocessing_metadata(
starting_metadata = current_samplesheet, id_column = "hpgl_identifier",
specification = first_spec, new_metadata = "persistence_hs_modified.xlsx",
basedir = "preprocessing", species = c("hg38_115", "lpanamensis_mhomcol_v68"))## Dropped 47 rows from the sample metadata because the sample ID is blank.## There are 2 duplicate rows in the sample ID column.## Did not find the condition column in the sample sheet.## Filling it in as undefined.## Did not find the batch column in the sample sheet.## Filling it in as undefined.## Checking the state of the condition column.## Checking the state of the batch column.## Checking the condition factor.## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
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## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA
## Warning in mean.default(input_df[[column]], na.rm = TRUE): argument is not numeric or logical: returning NA## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument is not numeric or logical: returning NA## Warning in dispatch_regex_search(meta, search, replace, input_file_spec, : NAs introduced by coercion
## Warning in dispatch_regex_search(meta, search, replace, input_file_spec, : NAs introduced by coercion## Writing new metadata to: persistence_hs_modified.xlsx## Deleting the file persistence_hs_modified.xlsx before writing the tables.## negative positive NAs
## 115 21 70## I think I will force everything to lowercase no spaces
modified_meta <- pre_meta[["new_meta"]]
## Added the following line to gather_preprocessing_metadata()
rownames(modified_meta) <- make.names(modified_meta[["hpgl_identifier"]], unique = TRUE)
## FIXME: 202511: I broke something in some of these functions and it is pulling
## the wrong information for number of observed genes.
head(modified_meta)## seq hpgl_identifier aim participant_code sample_type tube_label_origin number_of_vials sample_collection_date exp_person
## PRHU0001 1 PRHU0001 aim1 PP1006 PBMCs PP1006 PBMCs 1 2020-01-28 LG
## PRHU0002 2 PRHU0002 aim1 PP2001 nasal swab PP2001 HN 1 2020-02-04 LG
## PRHU0009 4 PRHU0009 aim1 PP2003 PBMCs PP2003 PBMCs 1 <NA> LG
## PRHU0010 5 PRHU0010 aim1 PP2004 PBMCs PP2004 PBMCs 1 2020-04-04 LG
## PRHU0011 6 PRHU0011 aim1 PP2005 PBMCs PP2005 PBMCs 1 2020-04-11 LG
## PRHU0018 7 PRHU0018 aim1 PP2003 WBCs PP2003 WBCs 1 2020-03-21 LG
## clinical_presentation parasite prior_host_hpgl_code prior_parasite_hpgl_code initial_recurrent
## PRHU0001 HD <NA> <NA> <NA> <NA>
## PRHU0002 HD <NA> <NA> <NA> <NA>
## PRHU0009 H-CL <NA> TMRC30130; TMRC30124; TMRC30131 <NA> <NA>
## PRHU0010 H-CL <NA> <NA> <NA> <NA>
## PRHU0011 H-CL <NA> <NA> <NA> <NA>
## PRHU0018 H-CL <NA> TMRC30130; TMRC30124; TMRC30131 <NA> <NA>
## drug_susceptibility_perc_reduction_gluc drug_susceptibility_perc_reduction_mlf zymodeme_by_electrophoresis zymodeme_by_pca
## PRHU0001 <NA> <NA> <NA> <NA>
## PRHU0002 <NA> <NA> <NA> <NA>
## PRHU0009 <NA> <NA> <NA> <NA>
## PRHU0010 <NA> <NA> <NA> <NA>
## PRHU0011 <NA> <NA> <NA> <NA>
## PRHU0018 <NA> <NA> <NA> <NA>
## rna_extraction_date rna_volume_ul rna_qc_date rna_nanodrop_ng_ul X260_280_ratio X260_230_ratio rna_bioanalyzer_or_qubit_ng_ul rin
## PRHU0001 2020-01-30 25 2020-02-14 338.43 2.04 1.73 407 NA
## PRHU0002 2020-02-13 25 2020-02-14 84.82 2.08 1.97 101 NA
## PRHU0009 2020-05-05 25 2020-05-05 115.1 1.99 1.34 167 8.0
## PRHU0010 2020-05-05 25 2020-05-05 185.28 2.03 1.91 264 8.5
## PRHU0011 2020-05-05 25 2020-05-05 70.12 1.95 0.85 113 8.7
## PRHU0018 2020-03-21 30 2020-05-01 49.55 2.06 1.56 70 9.1
## rna_qc_passed library_type library_version library_name library_const_date rna_used_to_construct_libraries_ul
## PRHU0001 yes mRNA 1 PP1006.PBMCs.mRNA.1 2020-02-08 1.48
## PRHU0002 yes mRNA 1 PP2001.nasal swab.mRNA.1 2020-02-14 3.54
## PRHU0009 yes mRNA 1 PP2003.PBMCs.mRNA.1 2020-06-04 1.79
## PRHU0010 yes mRNA 1 PP2004.PBMCs.mRNA.1 2020-06-04 1.14
## PRHU0011 yes mRNA 1 PP2005.PBMCs.mRNA.1 2020-06-04 2.65
## PRHU0018 yes mRNA 1 PP2003.WBCs.mRNA.1 2020-06-04 4.29
## rna_used_to_construct_libraries_ng library_qc_date lib_qc_passed library_volume_ul unique_dual_index_set
## PRHU0001 500 2020-02-11 yes 15 IDT for Illumina RNA UD Indexes Set A
## PRHU0002 300 2020-02-15 yes 15 IDT for Illumina RNA UD Indexes Set A
## PRHU0009 300 2020-06-10 yes 15 IDT for Illumina RNA UD Indexes Set A
## PRHU0010 300 2020-06-10 yes 15 IDT for Illumina RNA UD Indexes Set A
## PRHU0011 300 2020-06-10 yes 15 IDT for Illumina RNA UD Indexes Set A
## PRHU0018 300 2020-06-10 yes 15 IDT for Illumina RNA UD Indexes Set A
## unique_dual_index_plate_coordinate unique_dual_index_id concentrations_determined_by_1 primer_conc_ng_ul_30100bp_region_1
## PRHU0001 C04 UDP0027 TapeStation -
## PRHU0002 F04 UDP0030 TapeStation -
## PRHU0009 B05 UDP0034 Bioanalyzer 0.49
## PRHU0010 C05 UDP0035 Bioanalyzer 0.58
## PRHU0011 D05 UDP0036 Bioanalyzer 0.7
## PRHU0018 E05 UDP0037 Bioanalyzer 0.58
## adapter_dimer_conc_ng_ul_100200bp_region_1 library_conc_ng_ul_2001000bp_region_1 library_molarity_nm_2001000bp_region_1
## PRHU0001 - 69.50 321.0
## PRHU0002 - 17.50 82.3
## PRHU0009 0.27 60.64 293.3
## PRHU0010 0.35 64.84 314.0
## PRHU0011 0.27 56.86 279.9
## PRHU0018 0.12 85.33 421.9
## library_ave_frag_size_bp_2001000bp_region_1 calculated_adapter_dimer_percent_1 concentrations_determined_by_2
## PRHU0001 353 0 <NA>
## PRHU0002 345 0 <NA>
## PRHU0009 336 0.00445250659630607 <NA>
## PRHU0010 340 0.0053979025293029 <NA>
## PRHU0011 330 0.00474850510024622 <NA>
## PRHU0018 328 0.00140630493378648 <NA>
## primer_conc_ng_ul_30100bp_region_2 adapter_dimer_conc_ng_ul_100200bp_region_2 library_conc_ng_ul_2001000bp_region_2
## PRHU0001 <NA> <NA> NA
## PRHU0002 <NA> <NA> NA
## PRHU0009 <NA> <NA> NA
## PRHU0010 <NA> <NA> NA
## PRHU0011 <NA> <NA> NA
## PRHU0018 <NA> <NA> NA
## library_molarity_nm_2001000bp_region_2 library_ave_frag_size_bp_2001000bp_region_2 calculated_adapter_dimer_percent_2
## PRHU0001 NA NA NA
## PRHU0002 NA NA NA
## PRHU0009 NA NA NA
## PRHU0010 NA NA NA
## PRHU0011 NA NA NA
## PRHU0018 NA NA NA
## library_volume_ul_sent_to_umd shipment_date bbiagtc_date_received bbiagtc_library_cleanup bbiagtc_date_sequenced
## PRHU0001 14 2020-02-19 2020-04-04 not needed 2020-05-02
## PRHU0002 14 2020-02-19 2020-04-04 not needed 2020-05-02
## PRHU0009 14 2020-08-25 2020-11-04 not needed 2020-11-07
## PRHU0010 14 2020-08-25 2020-11-04 yes 2020-11-07
## PRHU0011 14 2020-08-25 2020-11-04 not needed 2020-11-07
## PRHU0018 14 2020-08-25 2020-11-04 not needed 2020-11-07
## bbiagtc_sequence_batch bbiagtc_pe_reads_pf bbiagtc_fastp_duplication_rate hisat2inputreads lpsingleconaligned hg38singleconaligned
## PRHU0001 PERS001 13167287 13.9 NA NA NA
## PRHU0002 PERS001 9815642 18.4 NA NA NA
## PRHU0009 PERS002 23614271 19.9 NA NA NA
## PRHU0010 PERS002 22777166 16.9 NA NA NA
## PRHU0011 PERS002 23458700 20.4 NA NA NA
## PRHU0018 PERS002 20907177 16.6 NA NA NA
## lpmulticonaligned hg38multiconaligned lpsingleallaligned hg38singleallaligned lpmultiallaligned hg38multiallaligned
## PRHU0001 NA NA NA NA NA NA
## PRHU0002 NA NA NA NA NA NA
## PRHU0009 NA NA NA NA NA NA
## PRHU0010 NA NA NA NA NA NA
## PRHU0011 NA NA NA NA NA NA
## PRHU0018 NA NA NA NA NA NA
## lppercentmappedfromlog hg38percentmappedfromlog lphisatobservedcds hg38hisatobservedcds krakenmatrix lphisatgenematrix
## PRHU0001 NA NA NA NA <NA> <NA>
## PRHU0002 NA NA NA NA <NA> <NA>
## PRHU0009 NA NA NA NA <NA> <NA>
## PRHU0010 NA NA NA NA <NA> <NA>
## PRHU0011 NA NA NA NA <NA> <NA>
## PRHU0018 NA NA NA NA <NA> <NA>
## hg38hisatgenematrix detectionparasiteby7sl detectionparasiteby18s detectionparasitebykdnasec metabolomicnasalswaborplasma
## PRHU0001 <NA> negative <NA> <NA> <NA>
## PRHU0002 <NA> negative <NA> <NA> yes
## PRHU0009 <NA> negative <NA> <NA> yes
## PRHU0010 <NA> negative <NA> <NA> yes
## PRHU0011 <NA> positive <NA> <NA> yes
## PRHU0018 <NA> positive positive <NA> yes
## immunophenotypingnasalswaborpbmcs condition batch sampleid trimomatic_input trimomatic_output trimomatic_percent fastqc_pct_gc
## PRHU0001 <NA> undefined undefined PRHU0001 13167287 12085247 0.918 49
## PRHU0002 <NA> undefined undefined PRHU0002 NA NA NA 50
## PRHU0009 <NA> undefined undefined PRHU0009 23614271 22272928 0.943 51
## PRHU0010 <NA> undefined undefined PRHU0010 22777166 21621102 0.949 51
## PRHU0011 <NA> undefined undefined PRHU0011 23458700 22252717 0.949 51
## PRHU0018 <NA> undefined undefined PRHU0018 20907177 19815284 0.948 52
## fastp_stats_fastp_version fastp_stats_sequencing fastp_stats_before_filtering.total_reads
## PRHU0001 0.26.0 paired end (59 cycles + 59 cycles) 26334574
## PRHU0002 0.26.0 paired end (59 cycles + 59 cycles) 19631284
## PRHU0009 0.26.0 paired end (59 cycles + 59 cycles) 47228542
## PRHU0010 0.26.0 paired end (59 cycles + 59 cycles) 45554332
## PRHU0011 0.26.0 paired end (59 cycles + 59 cycles) 46917400
## PRHU0018 0.26.0 paired end (59 cycles + 59 cycles) 41814354
## fastp_stats_before_filtering.total_bases fastp_stats_before_filtering.q20_bases fastp_stats_before_filtering.q30_bases
## PRHU0001 1537141332 1483778476 1421391509
## PRHU0002 1146889898 1107692660 1060592786
## PRHU0009 2751413261 2705939886 2609086781
## PRHU0010 2660310891 2625708398 2532148717
## PRHU0011 2737124181 2698488467 2603383176
## PRHU0018 2441104816 2408286992 2320982582
## fastp_stats_before_filtering.q20_rate fastp_stats_before_filtering.q30_rate fastp_stats_before_filtering.read1_mean_length
## PRHU0001 0.965284 0.924698 58
## PRHU0002 0.965823 0.924756 58
## PRHU0009 0.983473 0.948272 58
## PRHU0010 0.986993 0.951824 58
## PRHU0011 0.985885 0.951138 58
## PRHU0018 0.986556 0.950792 58
## fastp_stats_before_filtering.read2_mean_length fastp_stats_before_filtering.gc_content fastp_stats_after_filtering.total_reads
## PRHU0001 58 0.495877 22544922
## PRHU0002 58 0.497415 15922490
## PRHU0009 58 0.512464 37529230
## PRHU0010 58 0.515926 37574754
## PRHU0011 58 0.519911 37095802
## PRHU0018 58 0.52082 34639776
## fastp_stats_after_filtering.total_bases fastp_stats_after_filtering.q20_bases fastp_stats_after_filtering.q30_bases
## PRHU0001 1318104348 1276124238 1221709944
## PRHU0002 930885102 900358964 861357979
## PRHU0009 2193048423 2169861196 2090875519
## PRHU0010 2196201730 2173535393 2095503252
## PRHU0011 2167873036 2145364147 2067968633
## PRHU0018 2024201869 2002864759 1929726103
## fastp_stats_after_filtering.q20_rate fastp_stats_after_filtering.q30_rate fastp_stats_after_filtering.read1_mean_length
## PRHU0001 0.968151 0.926869 58
## PRHU0002 0.967207 0.925311 58
## PRHU0009 0.989427 0.953411 58
## PRHU0010 0.989679 0.954149 58
## PRHU0011 0.989617 0.953916 58
## PRHU0018 0.989459 0.953327 58
## fastp_stats_after_filtering.read2_mean_length fastp_stats_after_filtering.gc_content fastp_stats_passed_filter_reads
## PRHU0001 58 0.498084 26059422
## PRHU0002 58 0.502073 19463292
## PRHU0009 58 0.516925 46465958
## PRHU0010 58 0.518235 45101000
## PRHU0011 58 0.524196 46357106
## PRHU0018 58 0.523151 41388904
## fastp_stats_corrected_reads fastp_stats_corrected_bases fastp_stats_low_quality_reads fastp_stats_too_many_N_reads
## PRHU0001 34317 44064 256456 120
## PRHU0002 24196 31069 153482 76
## PRHU0009 96116 117982 683466 554
## PRHU0010 85367 105586 392346 318
## PRHU0011 82299 100241 498760 424
## PRHU0018 84087 103069 375302 474
## fastp_stats_low_complexity_reads fastp_stats_too_short_reads fastp_stats_too_long_reads fastp_stats_rate
## PRHU0001 18576 0 0 0.138303
## PRHU0002 14434 0 0 0.183586
## PRHU0009 78564 0 0 0.198086
## PRHU0010 60668 0 0 0.168274
## PRHU0011 61110 0 0 0.202799
## PRHU0018 49674 0 0 0.16479
## fastp_stats_adapter_trimmed_reads fastp_stats_adapter_trimmed_bases fastp_stats_read1_adapter_sequence
## PRHU0001 81032 321971 unspecified
## PRHU0002 55222 384645 unspecified
## PRHU0009 239054 698954 unspecified
## PRHU0010 152840 578602 unspecified
## PRHU0011 174070 623811 unspecified
## PRHU0018 177134 669440 unspecified
## fastp_stats_read2_adapter_sequence fastp_stats_read1_adapter_counts.A fastp_stats_read1_adapter_counts.others
## PRHU0001 unspecified 31766 8615
## PRHU0002 unspecified 15707 11739
## PRHU0009 unspecified 101534 17707
## PRHU0010 unspecified 60488 15632
## PRHU0011 unspecified 70256 16520
## PRHU0018 unspecified 69867 18417
## fastp_stats_read2_adapter_counts.A fastp_stats_read2_adapter_counts.others fastp_stats_read1_adapter_counts.G
## PRHU0001 31833 8683 undef
## PRHU0002 15863 11748 undef
## PRHU0009 101948 17579 undef
## PRHU0010 60796 15624 undef
## PRHU0011 70348 16687 undef
## PRHU0018 70321 18246 undef
## fastp_stats_read1_adapter_counts.AN fastp_stats_read1_adapter_counts.ANN fastp_stats_read2_adapter_counts.AN
## PRHU0001 undef undef undef
## PRHU0002 undef undef undef
## PRHU0009 undef undef undef
## PRHU0010 undef undef undef
## PRHU0011 undef undef undef
## PRHU0018 undef undef undef
## fastp_stats_read2_adapter_counts.ANN fastp_stats_read1_adapter_counts.C fastp_stats_read1_adapter_counts.T
## PRHU0001 undef undef undef
## PRHU0002 undef undef undef
## PRHU0009 undef undef undef
## PRHU0010 undef undef undef
## PRHU0011 undef undef undef
## PRHU0018 undef undef undef
## fastp_stats_read1_adapter_counts.ANNN fastp_stats_read2_adapter_counts.C fastp_stats_read2_adapter_counts.ANNN
## PRHU0001 undef undef undef
## PRHU0002 undef undef undef
## PRHU0009 undef undef undef
## PRHU0010 undef undef undef
## PRHU0011 undef undef undef
## PRHU0018 undef undef undef
## fastp_stats_read2_adapter_counts.G fastp_stats_read1_adapter_counts.N fastp_stats_read1_adapter_counts.NN
## PRHU0001 undef undef undef
## PRHU0002 undef undef undef
## PRHU0009 undef undef undef
## PRHU0010 undef undef undef
## PRHU0011 undef undef undef
## PRHU0018 undef undef undef
## fastp_stats_read1_adapter_counts.NNN fastp_stats_read1_adapter_counts.NNNN fastp_stats_read2_adapter_counts.N
## PRHU0001 undef undef undef
## PRHU0002 undef undef undef
## PRHU0009 undef undef undef
## PRHU0010 undef undef undef
## PRHU0011 undef undef undef
## PRHU0018 undef undef undef
## fastp_stats_read2_adapter_counts.GC fastp_stats_read2_adapter_counts.NN fastp_stats_read2_adapter_counts.NNN
## PRHU0001 undef undef undef
## PRHU0002 undef undef undef
## PRHU0009 undef undef undef
## PRHU0010 undef undef undef
## PRHU0011 undef undef undef
## PRHU0018 undef undef undef
## fastp_stats_read2_adapter_counts.NNNN fastp_stats_read2_adapter_counts.ACTGTCT fastp_stats_read2_adapter_counts.ACTGTCTAT
## PRHU0001 undef undef undef
## PRHU0002 undef undef undef
## PRHU0009 undef undef undef
## PRHU0010 undef undef undef
## PRHU0011 undef undef undef
## PRHU0018 undef undef undef
## kraken_bacterial_classified kraken_bacterial_unclassified kraken_first_bacterial_species kraken_first_bacterial_species_reads
## PRHU0001 418110 11667137 Staphylococcus aureus 117628
## PRHU0002 324998 8681618 Bacillus thuringiensis 48125
## PRHU0009 86840 866454 Porphyrobacter sp. GA68 11937
## PRHU0010 76438 1056370 Klebsiella pneumoniae 7070
## PRHU0011 70332 803596 Klebsiella pneumoniae 6002
## PRHU0018 46603 619808 Priestia megaterium 7831
## kraken_viral_classified kraken_viral_unclassified kraken_first_viral_species kraken_first_viral_species_reads
## PRHU0001 58135 12027112 Proteus virus Isfahan 35851
## PRHU0002 43789 8962827 Proteus virus Isfahan 28527
## PRHU0009 197113 22075815 Proteus virus Isfahan 146195
## PRHU0010 132560 21488542 Proteus virus Isfahan 90884
## PRHU0011 204047 22048670 Proteus virus Isfahan 160954
## PRHU0018 108172 19707112 Proteus virus Isfahan 74208
## kraken_matrix_viral
## PRHU0001 preprocessing/PRHU0001/outputs/20250918kraken_viral/kraken_report_matrix.tsv
## PRHU0002 preprocessing/PRHU0002/outputs/11kraken_viral/kraken_report_matrix.tsv
## PRHU0009 preprocessing/PRHU0009/outputs/20250918kraken_viral/kraken_report_matrix.tsv
## PRHU0010 preprocessing/PRHU0010/outputs/20250918kraken_viral/kraken_report_matrix.tsv
## PRHU0011 preprocessing/PRHU0011/outputs/20250918kraken_viral/kraken_report_matrix.tsv
## PRHU0018 preprocessing/PRHU0018/outputs/20250918kraken_viral/kraken_report_matrix.tsv
## kraken_matrix_bacterial pattern_match_reads_searched pattern_match_sl_observed
## PRHU0001 preprocessing/PRHU0001/outputs/02kraken_bacteria/kraken_report_matrix.tsv 12085247 NA
## PRHU0002 preprocessing/PRHU0002/outputs/02kraken_bacteria/kraken_report_matrix.tsv 9006616 NA
## PRHU0009 preprocessing/PRHU0009/outputs/06kraken_bacteria/kraken_report_matrix.tsv 22272928 NA
## PRHU0010 preprocessing/PRHU0010/outputs/06kraken_bacteria/kraken_report_matrix.tsv 21621102 NA
## PRHU0011 preprocessing/PRHU0011/outputs/06kraken_bacteria/kraken_report_matrix.tsv 22252717 NA
## PRHU0018 preprocessing/PRHU0018/outputs/06kraken_bacteria/kraken_report_matrix.tsv 19815284 NA
## pattern_match_sl_forward pattern_match_polyA_observed pattern_match_polyA_forward pattern_match_polyA_revcomp
## PRHU0001 NA NA NA NA
## PRHU0002 NA NA NA NA
## PRHU0009 NA NA NA NA
## PRHU0010 NA NA NA NA
## PRHU0011 NA NA NA NA
## PRHU0018 NA NA NA NA
## pattern_match_proportion_SL hisat_rrna_input_reads_hg38_115 hisat_rrna_input_reads_lpanamensis_mhomcol_v68
## PRHU0001 NA 12085247 NA
## PRHU0002 NA 9006616 NA
## PRHU0009 NA 22272928 NA
## PRHU0010 NA 21621102 NA
## PRHU0011 NA 22252717 NA
## PRHU0018 NA 19815284 NA
## hisat_rrna_single_concordant_hg38_115 hisat_rrna_single_concordant_lpanamensis_mhomcol_v68 hisat_rrna_multi_concordant_hg38_115
## PRHU0001 73426 NA 0
## PRHU0002 80682 NA 3
## PRHU0009 408744 NA 40
## PRHU0010 149371 NA 6
## PRHU0011 200482 NA 1
## PRHU0018 117268 NA 3
## hisat_rrna_multi_concordant_lpanamensis_mhomcol_v68 hisat_rrna_percent_log_hg38_115 hisat_rrna_percent_log_lpanamensis_mhomcol_v68
## PRHU0001 NA 0.63 NA
## PRHU0002 NA 0.94 NA
## PRHU0009 NA 1.93 NA
## PRHU0010 NA 0.73 NA
## PRHU0011 NA 0.94 NA
## PRHU0018 NA 0.62 NA
## hisat_genome_input_reads_hg38_115 hisat_genome_input_reads_lpanamensis_mhomcol_v68 hisat_genome_single_concordant_hg38_115
## PRHU0001 12085247 12085247 11027565
## PRHU0002 9006616 9006616 8119315
## PRHU0009 22272928 22272928 19763135
## PRHU0010 21621102 21621102 19276929
## PRHU0011 22252717 22252717 19973324
## PRHU0018 19815284 19815284 18103721
## hisat_genome_single_concordant_lpanamensis_mhomcol_v68 hisat_genome_multi_concordant_hg38_115
## PRHU0001 838 621289
## PRHU0002 760 505363
## PRHU0009 1382 1516414
## PRHU0010 559 1171082
## PRHU0011 662 1356659
## PRHU0018 415 1010925
## hisat_genome_multi_concordant_lpanamensis_mhomcol_v68 hisat_genome_single_all_hg38_115
## PRHU0001 99 365382
## PRHU0002 102 282669
## PRHU0009 102 790119
## PRHU0010 96 920497
## PRHU0011 82 758139
## PRHU0018 62 536623
## hisat_genome_single_all_lpanamensis_mhomcol_v68 hisat_genome_multi_all_hg38_115 hisat_genome_multi_all_lpanamensis_mhomcol_v68
## PRHU0001 14329 84362 6702
## PRHU0002 13432 71982 7507
## PRHU0009 22507 235198 17740
## PRHU0010 10941 242320 12474
## PRHU0011 12590 206128 13330
## PRHU0018 8241 133869 9468
## hisat_unmapped_hg38_115 hisat_unmapped_lpanamensis_mhomcol_v68 hisat_genome_percent_log_hg38_115
## PRHU0001 36996 24147571 99.85
## PRHU0002 131347 17990551 99.27
## PRHU0009 220613 44502561 99.50
## PRHU0010 189403 43217431 99.56
## PRHU0011 233569 44477964 99.48
## PRHU0018 177472 39611887 99.55
## hisat_genome_percent_log_lpanamensis_mhomcol_v68 hisat_observed_genes_hg38_115 hisat_observed_genes_lpanamensis_mhomcol_v68
## PRHU0001 0.09 15025 7
## PRHU0002 0.13 15857 8
## PRHU0009 0.10 15625 4
## PRHU0010 0.06 15627 3
## PRHU0011 0.06 15722 2
## PRHU0018 0.05 15210 3
## hisat_observed_median_exprs_hg38_115 hisat_observed_median_exprs_lpanamensis_mhomcol_v68
## PRHU0001 <NA> <NA>
## PRHU0002 <NA> <NA>
## PRHU0009 gene:ENSG00000163297\t4\t79901146\t80125454\t-\t224309\t1060 <NA>
## PRHU0010 <NA> <NA>
## PRHU0011 <NA> <NA>
## PRHU0018 <NA> <NA>
## hisat_alignment_hg38_115
## PRHU0001 preprocessing/PRHU0001/outputs/20250918hisat_hg38_115/hg38_115_genome.bam
## PRHU0002 preprocessing/PRHU0002/outputs/20250918hisat_hg38_115/hg38_115_genome.bam
## PRHU0009 preprocessing/PRHU0009/outputs/20250918hisat_hg38_115/hg38_115_genome.bam
## PRHU0010 preprocessing/PRHU0010/outputs/20250918hisat_hg38_115/hg38_115_genome.bam
## PRHU0011 preprocessing/PRHU0011/outputs/20250918hisat_hg38_115/hg38_115_genome.bam
## PRHU0018 preprocessing/PRHU0018/outputs/20250918hisat_hg38_115/hg38_115_genome.bam
## hisat_alignment_lpanamensis_mhomcol_v68 salmon_mapped_hg38_115
## PRHU0001 preprocessing/PRHU0001/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome.bam NA
## PRHU0002 preprocessing/PRHU0002/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome.bam NA
## PRHU0009 preprocessing/PRHU0009/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome.bam NA
## PRHU0010 preprocessing/PRHU0010/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome.bam NA
## PRHU0011 preprocessing/PRHU0011/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome.bam NA
## PRHU0018 preprocessing/PRHU0018/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome.bam NA
## salmon_mapped_lpanamensis_mhomcol_v68 salmon_percent_hg38_115 salmon_percent_lpanamensis_mhomcol_v68 salmon_observed_genes_hg38_115
## PRHU0001 228 54.6219 0.00188660 40892
## PRHU0002 NA 61.0939 0.02397130 37639
## PRHU0009 514 53.7645 0.00230773 47176
## PRHU0010 532 55.2906 0.00246056 47162
## PRHU0011 564 56.4814 0.00253452 46983
## PRHU0018 411 57.3811 0.00207416 43731
## salmon_observed_genes_lpanamensis_mhomcol_v68 input_r1 input_r2
## PRHU0001 12 unprocessed/PRHU0001_S49_R1_001.fastq.gz unprocessed/PRHU0001_S49_R2_001.fastq.gz
## PRHU0002 12
## PRHU0009 14 unprocessed/PRHU0009_S7_R1_001.fastq.gz unprocessed/PRHU0009_S7_R2_001.fastq.gz
## PRHU0010 13 unprocessed/PRHU0010_S8_R1_001.fastq.gz unprocessed/PRHU0010_S8_R2_001.fastq.gz
## PRHU0011 15 unprocessed/PRHU0011_S9_R1_001.fastq.gz unprocessed/PRHU0011_S9_R2_001.fastq.gz
## PRHU0018 14 unprocessed/PRHU0018_S16_R1_001.fastq.gz unprocessed/PRHU0018_S16_R2_001.fastq.gz
## hisat_count_table_hg38_115
## PRHU0001 preprocessing/PRHU0001/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## PRHU0002 preprocessing/PRHU0002/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## PRHU0009 preprocessing/PRHU0009/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## PRHU0010 preprocessing/PRHU0010/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## PRHU0011 preprocessing/PRHU0011/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## PRHU0018 preprocessing/PRHU0018/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## hisat_count_table_lpanamensis_mhomcol_v68
## PRHU0001 preprocessing/PRHU0001/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome-paired_s2_protein_coding_gene_ID_fcounts.csv.xz
## PRHU0002 preprocessing/PRHU0002/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome-paired_s2_protein_coding_gene_ID_fcounts.csv.xz
## PRHU0009 preprocessing/PRHU0009/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome-paired_s2_protein_coding_gene_ID_fcounts.csv.xz
## PRHU0010 preprocessing/PRHU0010/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome-paired_s2_protein_coding_gene_ID_fcounts.csv.xz
## PRHU0011 preprocessing/PRHU0011/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome-paired_s2_protein_coding_gene_ID_fcounts.csv.xz
## PRHU0018 preprocessing/PRHU0018/outputs/20250918hisat_lpanamensis_mhomcol_v68/lpanamensis_mhomcol_v68_genome-paired_s2_protein_coding_gene_ID_fcounts.csv.xz
## salmon_count_table_hg38_115
## PRHU0001 preprocessing/PRHU0001/outputs/80salmon_hg38_115_CDS/quant.sf
## PRHU0002 preprocessing/PRHU0002/outputs/80salmon_hg38_115_CDS/quant.sf
## PRHU0009 preprocessing/PRHU0009/outputs/80salmon_hg38_115_CDS/quant.sf
## PRHU0010 preprocessing/PRHU0010/outputs/80salmon_hg38_115_CDS/quant.sf
## PRHU0011 preprocessing/PRHU0011/outputs/80salmon_hg38_115_CDS/quant.sf
## PRHU0018 preprocessing/PRHU0018/outputs/80salmon_hg38_115_CDS/quant.sf
## salmon_count_table_lpanamensis_mhomcol_v68
## PRHU0001 preprocessing/PRHU0001/outputs/20250918salmon_lpanamensis_mhomcol_v68_CDS/quant.sf
## PRHU0002 preprocessing/PRHU0002/outputs/20250918salmon_lpanamensis_mhomcol_v68_CDS/quant.sf
## PRHU0009 preprocessing/PRHU0009/outputs/20250918salmon_lpanamensis_mhomcol_v68_CDS/quant.sf
## PRHU0010 preprocessing/PRHU0010/outputs/20250918salmon_lpanamensis_mhomcol_v68_CDS/quant.sf
## PRHU0011 preprocessing/PRHU0011/outputs/20250918salmon_lpanamensis_mhomcol_v68_CDS/quant.sf
## PRHU0018 preprocessing/PRHU0018/outputs/20250918salmon_lpanamensis_mhomcol_v68_CDS/quant.sf## [1] 40892 37639 47176 47162 46983 43731modified_meta[["detectionparasiteby7sl"]] <- sanitize_metadata(
modified_meta[["detectionparasiteby7sl"]], lower = TRUE, spaces = TRUE, punct = FALSE)
summary(modified_meta[["detectionparasiteby7sl"]])## negative notapplicable positive
## 115 70 21I would like to have a combination of clinical presentation and sampletype as well:
modified_meta[["sample_type"]] <- sanitize_metadata(
modified_meta[["sample_type"]], lower = TRUE, spaces = TRUE, punct = FALSE)
summary(modified_meta[["sample_type"]])## lesionswab macrophages nasalswab par parasite pbmcs
## 5 13 30 16 31 30
## skinbiopsynon-lesion skinbiopsyscar wbcs
## 29 22 30modified_meta[["clinical_presentation"]] <- sanitize_metadata(
modified_meta[["clinical_presentation"]], lower = TRUE, spaces = TRUE, punct = FALSE)
summary(modified_meta[["clinical_presentation"]])## cl h-cl hd notapplicable
## 5 149 24 28modified_meta[["type_presentation"]] <- as.factor(paste0(as.character(modified_meta[["sample_type"]]), "_",
as.character(modified_meta[["clinical_presentation"]])))
summary(modified_meta[["type_presentation"]])## lesionswab_cl macrophages_notapplicable nasalswab_h-cl nasalswab_hd par_h-cl
## 5 13 21 9 16
## parasite_h-cl parasite_notapplicable pbmcs_h-cl pbmcs_hd skinbiopsynon-lesion_h-cl
## 16 15 25 5 23
## skinbiopsynon-lesion_hd skinbiopsyscar_h-cl wbcs_h-cl wbcs_hd
## 6 22 26 4Create a factor of the 7SL detection of nasal samples. Note, we need to recast the NAs as undefined. I would have sworn that my gather function would do that?
modified_meta[["nasal_7sl_status"]] <- as.character(modified_meta[["detectionparasiteby7sl"]])
nasal_samples <- modified_meta[["sample_type"]] == "nasalswab"
summary(nasal_samples)## Mode FALSE TRUE
## logical 176 30## Mode FALSE TRUE
## logical 185 21## Mode FALSE TRUE
## logical 196 10nasal_positive_samples <- rownames(modified_meta)[nasal_positive]
nasal_positive_people <- modified_meta[nasal_positive_samples, "participant_code"]
nasal_positive_people## [1] "PP1009" "PP2020" "PP1009" "PP2005" "PP2006" "PP2019" "PP2020" "PP2005" "PP2019" "PP2005"nasal_positive_people_samples <- modified_meta[["participant_code"]] %in% nasal_positive_people
modified_meta[["nasal_7sl_status"]] <- "negative"
modified_meta[nasal_positive_people_samples, "nasal_7sl_status"] <- "positive"
modified_meta[["nasal_7sl_status"]] <- as.factor(modified_meta[["nasal_7sl_status"]])
summary(modified_meta[["nasal_7sl_status"]])## negative positive
## 173 33Similarly create a factor of people with any 7SL positive sample.
modified_meta[["any_7sl_status"]] <- "negative"
positive_samples <- as.character(modified_meta[["detectionparasiteby7sl"]]) == "positive"
positive_people <- modified_meta[positive_samples, "participant_code"]
positive_people_idx <- !duplicated(positive_people)
positive_people_sorted <- sort(positive_people[positive_people_idx])
persister_people_samples <- modified_meta[["participant_code"]] %in% positive_people_sorted
modified_meta[persister_people_samples, "any_7sl_status"] <- "positive"
modified_meta[["any_7sl_status"]] <- as.factor(modified_meta[["any_7sl_status"]])
summary(modified_meta[["any_7sl_status"]])## negative positive
## 147 59## [1] "PP1009" "PP1018" "PP1023" "PP2003" "PP2005" "PP2006" "PP2019" "PP2020" "PP2021" "PP2022" "PP2026"4.1 Add combination of detection and library type
Also add a category separating all skin samples from everything else.
modified_meta[["detection_type"]] <- as.factor(
paste0(as.character(modified_meta[["detectionparasiteby7sl"]]), "_",
as.character(modified_meta[["library_type"]])))
summary(modified_meta[["detection_type"]])## negative_mRNA negative_RZ negative_SL1 negative_SL2 notapplicable_mRNA notapplicable_RZ notapplicable_SL1
## 106 7 1 1 46 1 15
## notapplicable_SL2 positive_mRNA positive_RZ positive_SL2
## 8 13 5 3modified_meta[["skinp"]] <- "not_skin"
skin_idx <- grepl(x = modified_meta[["sample_type"]], pattern = "^skin")
summary(skin_idx)## Mode FALSE TRUE
## logical 155 51modified_meta[skin_idx, "skinp"] <- "skin"
modified_meta[["skinp"]] <- as.factor(modified_meta[["skinp"]])
summary(modified_meta[["skinp"]])## not_skin skin
## 155 515 The purpose of the post 202608 samples
In the last round of sequencing of human samples, we received a series of purely healthy samples; these are from individuals with no history of Leishmaniasis and are intended to provide a comparison point against everything else we are looking at in this dataset.
human_meta_idx <- modified_meta[["aim"]] == "aim1"
human_meta <- modified_meta[human_meta_idx, ]
table(human_meta[["clinical_presentation"]])##
## cl h-cl hd notapplicable
## 0 113 20 0##
## lesionswab_cl macrophages_notapplicable nasalswab_h-cl nasalswab_hd par_h-cl
## 0 0 19 7 0
## parasite_h-cl parasite_notapplicable pbmcs_h-cl pbmcs_hd skinbiopsynon-lesion_h-cl
## 0 0 25 5 23
## skinbiopsynon-lesion_hd skinbiopsyscar_h-cl wbcs_h-cl wbcs_hd
## 4 21 25 4Thus, in theory we can perform a series of comparisons among the ‘Clinical Presentation’ factor in the sample design. By itself, this would give us a 20 vs. 113 sample comparison of healthy people vs. people who came to the clinic.
6 Collect gene annotations
I should have all my load_xyz_annotation functions return some of the same elements in their retlists.
7 Quick peek at the SL samples, hg38 release 115
7.1 Gather the transcript and gene annotations.
7.2 Figure out which samples do not have 7SL categories
## Mode FALSE
## logical 133sample_7sl <- paste0(human_meta[["sample_type"]], "_",
human_meta[["detectionparasiteby7sl"]])
sample_7sl <- as.factor(gsub(x = sample_7sl, pattern = "[[:space:]]", replacement = "_"))
summary(sample_7sl)## nasalswab_negative nasalswab_notapplicable nasalswab_positive pbmcs_negative
## 16 2 8 26
## pbmcs_positive skinbiopsynon-lesion_negative skinbiopsynon-lesion_positive skinbiopsyscar_negative
## 4 26 1 21
## wbcs_negative wbcs_positive
## 24 57.3 Healthy vs scar samples
healthy_vs_scar <- gsub(x = human_meta[["sample_type"]],
pattern = "^skinbiopsy ", replacement = "")
human_meta[["hs"]] <- NA
skin_samples <- grepl(x = human_meta[["sample_type"]], pattern = "^skin")
human_meta[skin_samples, "hs"] <- "healthy"
scar_samples <- grepl(x = human_meta[["sample_type"]], pattern = "scar")
human_meta[scar_samples, "hs"] <- "scar"
human_meta[["hs"]] <- as.factor(human_meta[["hs"]])
summary(human_meta[["hs"]])## healthy scar NAs
## 27 21 857.4 Clean up missing 7SL detection samples
I think the last of these has been fixed since the last time I updated this sheet.
## Error in `h()`:
## ! error in evaluating the argument 'x' in selecting a method for function 'colData': object 'hs_hisat' not found## Mode FALSE
## logical 133if (sum(undef_7sl)) {
colData(hs_salmon)[undef_7sl, "detectionparasiteby7sl"] <- "unknown"
colData(hs_hisat)[undef_7sl, "detectionparasiteby7sl"] <- "unknown"
} else {
message("There appear to be no missing 7SL entries.")
}## There appear to be no missing 7SL entries.7.5 Print current state
##
## mRNA RZ
## 120 13##
## negative_mRNA negative_RZ negative_SL1 negative_SL2 notapplicable_mRNA notapplicable_RZ notapplicable_SL1
## 106 7 0 0 1 1 0
## notapplicable_SL2 positive_mRNA positive_RZ positive_SL2
## 0 13 5 0##
## negative notapplicable positive
## 113 2 18##
## cl h-cl hd notapplicable
## 0 113 20 07.6 Combine 7SL status and the sample type into one factor
sample_7sl <- as.factor(paste0(human_meta[["sample_type"]], "_",
human_meta[["detectionparasiteby7sl"]]))
summary(sample_7sl)## nasalswab_negative nasalswab_notapplicable nasalswab_positive pbmcs_negative
## 16 2 8 26
## pbmcs_positive skinbiopsynon-lesion_negative skinbiopsynon-lesion_positive skinbiopsyscar_negative
## 4 26 1 21
## wbcs_negative wbcs_positive
## 24 57.7 Create initial hisat/salmon tables
hs_salmon <- create_se(human_meta, gene_info = hg_tx,
tx_gene_map = hg_map, file_column = "salmon_count_table_hg38_115") %>%
set_conditions(fact = "sample_type") %>%
set_batches(fact = "library_type")## Reading the sample metadata.## Checking the state of the condition column.## Checking the state of the batch column.## Checking the condition factor.## The sample definitions comprises: 133 rows(samples) and 184 columns(metadata fields).## In some cases, (notably salmon) the format of the IDs used by this can be tricky.
## It is likely to require the transcript ID followed by a '.' and the ensembl column:
## 'transcript_version', which is explicitly different than the gene version column.
## If this is not correctly performed, very few genes will be observed## Rewriting the transcript<->gene map to remove tx versions.## reading in files with read_tsv## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103
## transcripts missing from tx2gene: 151231
## summarizing abundance
## summarizing counts
## summarizing length## Warning in create_se(human_meta, gene_info = hg_tx, tx_gene_map = hg_map, : Some samples were removed when cross referencing the samples
## against the count data.## Matched 10885 annotations and counts.
## Saving the summarized experiment to 'se.rda'.
## The final summarized experiment has 10885 rows and 184 columns.
## The numbers of samples by condition are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 20 25 19 16 23## Setting colors with no pre-defined colors, using the Dark2 palette.
## The number of samples by batch are:##
## mRNA RZ
## 91 12hs_hisat <- create_se(human_meta, gene_info = hg_genes,
file_column = "hisat_count_table_hg38_115") %>%
set_conditions(fact = "sample_type") %>%
set_batches(fact = "library_type")## Reading the sample metadata.
## Checking the state of the condition column.
## Checking the state of the batch column.
## Checking the condition factor.
## The sample definitions comprises: 133 rows(samples) and 184 columns(metadata fields).## Warning in create_se(human_meta, gene_info = hg_genes, file_column = "hisat_count_table_hg38_115"): Some samples were removed when cross
## referencing the samples against the count data.## Matched 21571 annotations and counts.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the summarized experiment to 'se.rda'.
## The final summarized experiment has 21571 rows and 184 columns.
## The numbers of samples by condition are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 25 30 27 21 29## Setting colors with no pre-defined colors, using the Dark2 palette.
## The number of samples by batch are:##
## mRNA RZ
## 120 127.7.1 Write out cpm values of all samples
hs_salmon_cpm <- write_normalized_se(
hs_salmon, excel = glue("excel/cpm/hs_salmon_all_samples-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Removing 3015 low-count genes (7870 remaining).## transform_counts: Found 494278 values equal to 0, adding 1 to the matrix.hs_hisat_cpm <- write_normalized_se(
hs_hisat, excel = glue("excel/cpm/hs_hisat_all_samples-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 165117 values equal to 0, adding 1 to the matrix.7.8 Separate the mRNA samples from ribo-zero
Note, when we are finished, we will be using only the mRNA samples and ignoring the ribo-zero. But there are some questions about the data provided by the two libraries.
hs_salmon_mrna <- set_conditions(hs_salmon, fact = "sample_type") %>%
subset_se(subset = "library_type=='mRNA'") %>%
set_batches("detectionparasiteby7sl")## The numbers of samples by condition are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 20 25 19 16 23## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## negative positive
## 81 10hs_salmon_rz <- set_conditions(hs_salmon, fact = "sample_type") %>%
subset_se(subset = "library_type=='RZ'") %>%
set_batches("detectionparasiteby7sl")## The numbers of samples by condition are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 20 25 19 16 23## Setting colors with no pre-defined colors, using the Dark2 palette.
## The number of samples by batch are:##
## negative positive
## 7 5hs_hisat_mrna <- set_conditions(hs_hisat, fact = "sample_type") %>%
subset_se(subset = "library_type=='mRNA'") %>%
set_batches("detectionparasiteby7sl")## The numbers of samples by condition are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 25 30 27 21 29## Setting colors with no pre-defined colors, using the Dark2 palette.
## The number of samples by batch are:##
## negative notapplicable positive
## 106 1 13hs_hisat_rz <- set_conditions(hs_hisat, fact = "sample_type") %>%
subset_se(subset = "library_type=='RZ'") %>%
set_batches("detectionparasiteby7sl")## The numbers of samples by condition are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 25 30 27 21 29## Setting colors with no pre-defined colors, using the Dark2 palette.
## The number of samples by batch are:##
## negative positive
## 7 57.8.1 Write those out as well
hs_salmon_mrna_cpm <- write_normalized_se(
hs_salmon_mrna, excel = glue("excel/cpm/hs_salmon_mrna_samples-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Removing 3570 low-count genes (7315 remaining).## transform_counts: Found 390200 values equal to 0, adding 1 to the matrix.hs_salmon_rz_cpm <- write_normalized_se(
hs_salmon_rz, excel = glue("excel/cpm/hs_salmon_rz_samples-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Removing 5861 low-count genes (5024 remaining).## transform_counts: Found 25004 values equal to 0, adding 1 to the matrix.hs_hisat_mrna_cpm <- write_normalized_se(
hs_hisat_mrna, excel = glue("excel/cpm/hs_hisat_mrna_samples-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Removing 5874 low-count genes (15697 remaining).## transform_counts: Found 140373 values equal to 0, adding 1 to the matrix.hs_hisat_rz_cpm <- write_normalized_se(
hs_hisat_rz, excel = glue("excel/cpm/hs_hisat_rz_samples-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Removing 7058 low-count genes (14513 remaining).## transform_counts: Found 3326 values equal to 0, adding 1 to the matrix.7.9 Extract only the healthy/scar samples, only mRNA
hs_salmon_mrna_health <- subset_se(hs_salmon, subset = "hs=='healthy'|hs=='scar'") %>%
set_conditions(fact = "hs")## The numbers of samples by condition are:##
## healthy scar
## 19 16## Setting colors with no pre-defined colors, using the Dark2 palette.hs_hisat_mrna_health <- subset_se(hs_hisat_mrna, subset = "hs=='healthy'|hs=='scar'") %>%
set_conditions(fact = "hs")## The numbers of samples by condition are:##
## healthy scar
## 25 20## Setting colors with no pre-defined colors, using the Dark2 palette.7.9.1 Write these out
hs_salmon_mrna_health_cpm <- write_normalized_se(
hs_salmon_mrna_health, excel = glue("excel/cpm/hs_salmon_mrna_health_samples-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Removing 5185 low-count genes (5700 remaining).## transform_counts: Found 100828 values equal to 0, adding 1 to the matrix.hs_hisat_mrna_health_cpm <- write_normalized_se(
hs_hisat_mrna_health, excel = glue("excel/cpm/hs_hisat_mrna_health_samples-v{ver}.xlsx"))## Writing the first sheet, containing a legend and some summary data.## Removing 7367 low-count genes (14204 remaining).## transform_counts: Found 5921 values equal to 0, adding 1 to the matrix.8 HU Metadata
8.1 Percent of reads mapped to the genome by hisat
hs_mapped_mrna <- plot_metadata_factor(hs_hisat_mrna, column = "hisat_genome_percent_log_hg38_115")
pp(file = "images/hs_mapped_mrna_violin.png", image = hs_mapped_mrna)
hs_mapped_rz <- plot_metadata_factor(hs_hisat_rz, column = "hisat_genome_percent_log_hg38_115")
pp(file = "images/hs_mapped_rz_violin.png", image = hs_mapped_rz)## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
## Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
8.2 Number of gene observed by salmon
hs_observed_mrna <- plot_metadata_factor(hs_hisat_mrna, column = "salmon_observed_genes_hg38_115")
hs_observed_mrna
hs_observed_rz <- plot_metadata_factor(hs_hisat_rz, column = "salmon_observed_genes_hg38_115")
hs_observed_rz## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
8.3 Percent reads quantified to the human transcriptome by salmon
## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
8.4 Number of kraken identified bacterial reads
While at this, add a column dividing the kraken bacterial classified reads from the total reads.
hs_kraken_mrna <- plot_metadata_factor(hs_salmon_mrna, column = "kraken_bacterial_classified")
pp(file = "images/hs_kraken_bacterial_reads_mrna_libraries.png", image = hs_kraken_mrna)
hs_kraken_rz <- plot_metadata_factor(hs_salmon_rz, column = "kraken_bacterial_classified")
pp(file = "images/hs_kraken_bacterial_reads_rz_libraries.png", image = hs_kraken_rz)## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
## Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
colData(hs_salmon_mrna)[["pct_kraken_bacterial"]] <- colData(hs_salmon_mrna)[["kraken_bacterial_classified"]] /
colData(hs_salmon_mrna)[["trimomatic_output"]]
colData(hs_salmon_rz)[["pct_kraken_bacterial"]] <- colData(hs_salmon_rz)[["kraken_bacterial_classified"]] /
colData(hs_salmon_rz)[["trimomatic_output"]]
hs_kraken_pct_mrna <- plot_metadata_factor(hs_salmon_mrna, column = "pct_kraken_bacterial")
pp(file = "images/hs_kraken_bacterial_pct_reads_mrna_libraries.png", image = hs_kraken_pct_mrna)## Warning: Removed 1 row containing non-finite outside the scale range (`stat_ydensity()`).## Warning: Removed 1 row containing non-finite outside the scale range (`stat_boxplot()`).## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_point()`).## Warning: Removed 1 row containing non-finite outside the scale range (`stat_ydensity()`).## Warning: Removed 1 row containing non-finite outside the scale range (`stat_boxplot()`).## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_point()`).
hs_kraken_pct_rz <- plot_metadata_factor(hs_salmon_rz, column = "pct_kraken_bacterial")
pp(file = "images/hs_kraken_bacterial_pct_reads_rz_libraries.png", image = hs_kraken_pct_rz)## Warning: Removed 1 row containing non-finite outside the scale range (`stat_ydensity()`).## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.## Warning: Removed 1 row containing non-finite outside the scale range (`stat_boxplot()`).## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_point()`).## Warning: Removed 1 row containing non-finite outside the scale range (`stat_ydensity()`).## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.## Warning: Removed 1 row containing non-finite outside the scale range (`stat_boxplot()`).## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_point()`).
8.5 Sankey of a few factors
hs_sankey <- plot_meta_sankey(
hs_salmon, factors = c("detectionparasiteby7sl", "sample_type", "library_type"))## Warning: attributes are not identical across measure variables; they will be dropped## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the ggsankey package.
## Please report the issue at <https://github.com/davidsjoberg/ggsankey/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
hs_sankey <- plot_meta_sankey(
hs_salmon, factors = c("sample_type", "detectionparasiteby7sl", "library_type"))## Warning: attributes are not identical across measure variables; they will be dropped
9 Write out the metadata in its current state
write_xlsx(data = modified_meta, excel = "sample_sheets/human_samples_202511_with_nasal_factor.xlsx")## Deleting the file sample_sheets/human_samples_202511_with_nasal_factor.xlsx before writing the tables.## write_xlsx() wrote sample_sheets/human_samples_202511_with_nasal_factor.xlsx.
## The cursor is on sheet first, row: 209 column: 212.10 nonzero/libsize/etc
## The colors used in the expressionset are: #1B9E77, #66A61E, #7570B3, #D95F02, #E7298A.
pp(file = "images/hs_salmon_quantreads.png", image = plot_libsize(hs_salmon, yscale = FALSE),
width = 24, height = 9)
## The following samples have less than 7075.25 features.## [1] "PRHU0001" "PRHU0002" "PRHU0009" "PRHU0010" "PRHU0011" "PRHU0018" "PRHU0019" "PRHU0020" "PRHU0012" "PRHU0013" "PRHU0014" "PRHU0021"
## [13] "PRHU0022" "PRHU0023" "PRHU0015" "PRHU0016" "PRHU0017" "PRHU0024" "PRHU0025" "PRHU0026" "PRHU0038" "PRHU0006" "PRHU0007" "PRHU0008"
## [25] "PRHU0005" "PRHU0004" "PRHU0003" "PRHU0027" "PRHU0028" "PRHU0029" "PRHU0030" "PRHU0031" "PRHU0032" "PRHU0035" "PRHU0033" "PRHU0034"
## [37] "PRHU0036" "PRHU0037" "PRHU0039" "PRHU0040" "PRHU0041" "PRHU0042" "PRHU0043" "PRHU0044" "PRHU0045" "PRHU0046" "PRHU0047" "PRHU0048"
## [49] "PRHU0049" "PRHU0050" "PRHU0051" "PRHU0052" "PRHU0053" "PRHU0054" "PRHU0055" "PRHU0056" "PRHU0057" "PRHU0058" "PRHU0059" "PRHU0060"
## [61] "PRHU0061" "PRHU0062" "PRHU0063" "PRHU0064" "PRHU0065" "PRHU0066" "PRHU0067" "PRHU0068" "PRHU0069" "PRHU0070" "PRHU0071" "PRHU0072"
## [73] "PRHU0073" "PRHU0074" "PRHU0075" "PRHU0076" "PRHU0077" "PRHU0078" "PRHU0079" "PRHU0080" "PRHU0081" "PRHU0082" "PRHU0083" "PRHU0084"
## [85] "PRHU0085" "PRHU0086" "PRHU0087" "PRHU0088" "PRHU0089" "PRHU0090" "PRHU0091" "PRHU0092" "PRHU0093" "PRHU0094" "PRHU0095" "PRHU0096"
## [97] "PRHU0097" "PRHU0098" "PRHU0099" "PRHU0100" "PRHU0101" "PRHU0102" "PRHU0103"## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the hpgltools package.
## Please report the issue to the authors.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
pp(file = "images/hs_hisat_quantreads.png", image = plot_libsize(hs_hisat, yscale = FALSE),
width = 24, height = 9)
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
11 Extract the nasal samples and see how many mRNA and ribozero are of the same person
hs_hisat_nasal <- subset_se(hs_hisat, subset = "sample_type=='nasalswab'") %>%
set_conditions(fact = "detectionparasiteby7sl")## The numbers of samples by condition are:##
## negative notapplicable positive
## 16 1 8## Setting colors with no pre-defined colors, using the Dark2 palette.person_libtype <- colData(hs_hisat_nasal)[, c("participant_code", "library_type")]
table(person_libtype)## library_type
## participant_code mRNA RZ
## PP1009 1 1
## PP1011 1 1
## PP1023 1 0
## PP1024 1 0
## PP1025 1 0
## PP1026 1 0
## PP1041 1 0
## PP1042 1 0
## PP1043 1 0
## PP1044 1 0
## PP2001 1 1
## PP2005 1 1
## PP2006 0 1
## PP2007 0 1
## PP2008 0 1
## PP2019 0 1
## PP2020 1 1
## PP2021 1 0
## PP2022 1 0
## PP2030 1 0## We have both library types for 4 people: 1009, 1011, 2001, and 2020.
person_libtype_model_nbb <- set_batches(hs_hisat_nasal, fact = "library_type") %>%
normalize(transform = "log2", convert = "cpm", batch = "pca", filter = TRUE) %>%
normalize(batch = "svaseq")## The number of samples by batch are:##
## mRNA RZ
## 16 9## Removing 7481 low-count genes (14090 remaining).## transform_counts: Found 1062 values less than 0.## transform_counts: Found 1062 values equal to 0, adding 1 to the matrix.## Setting 218 entries to zero.newnames <- colData(hs_hisat_nasal)[["participant_code"]]
pp(file = "images/pca_batch_then_sva.png",
image = plot_pca(person_libtype_model_nbb, exp_names = newnames, plot_labels = TRUE),
width = 12, height = 12)
person_presentation <- colData(hs_hisat_nasal)[, c("participant_code", "clinical_presentation")]
table(person_presentation)## clinical_presentation
## participant_code cl h-cl hd notapplicable
## PP1009 0 2 0 0
## PP1011 0 2 0 0
## PP1023 0 1 0 0
## PP1024 0 1 0 0
## PP1025 0 1 0 0
## PP1026 0 1 0 0
## PP1041 0 0 1 0
## PP1042 0 0 1 0
## PP1043 0 0 1 0
## PP1044 0 0 1 0
## PP2001 0 0 2 0
## PP2005 0 2 0 0
## PP2006 0 1 0 0
## PP2007 0 1 0 0
## PP2008 0 1 0 0
## PP2019 0 1 0 0
## PP2020 0 2 0 0
## PP2021 0 1 0 0
## PP2022 0 1 0 0
## PP2030 0 1 0 0## Note to self, person 2001 is our lone nasal healthy donor.
hs_hisat_nasal_nohealthy <- subset_se(hs_hisat_nasal, subset = "clinical_presentation!='HD'")
hs_hisat_nasal_nohealthy## class: SummarizedExperiment
## dim: 21571 25
## metadata(7): notes title ... study researcher
## assays(1): ''
## rownames(21571): ENSG00000000003 ENSG00000000005 ... ENSG00000310590 ENSG00000310592
## rowData names(11): ensembl_transcript_id version ... end_position hgnc_symbol
## colnames(25): PRHU0002 PRHU0038 ... PRHU0131 PRHU0119
## colData names(185): seq hpgl_identifier ... hs colornewnames <- colData(hs_hisat_nasal_nohealthy)[["participant_code"]]
hs_hisat_nasal_nh_norm <- normalize(hs_hisat_nasal_nohealthy, transform = "log2", convert = "cpm",
filter = TRUE, norm = "quant")## Removing 7481 low-count genes (14090 remaining).## transform_counts: Found 95 values equal to 0, adding 1 to the matrix.pp(file = "images/hs_hisat_nasal_nh_norm_pca.png",
image = plot_pca(hs_hisat_nasal_nh_norm, exp_names = newnames, plot_labels = TRUE))
hs_hisat_nasal_nh_nb <- normalize(hs_hisat_nasal_nohealthy, transform = "log2", convert = "cpm",
filter = TRUE, batch = "svaseq")## Removing 7481 low-count genes (14090 remaining).## transform_counts: Found 915 values less than 0.## transform_counts: Found 915 values equal to 0, adding 1 to the matrix.pp(file = "images/hs_hisat_nasal_nh_nb_pca.png",
image = plot_pca(hs_hisat_nasal_nh_nb, exp_names = newnames, plot_labels = TRUE))
## The number of samples by batch are:##
## PP1009 PP1011 PP1023 PP1024 PP1025 PP1026 PP1041 PP1042 PP1043 PP1044 PP2001 PP2005 PP2006 PP2007 PP2008 PP2019 PP2020 PP2021 PP2022 PP2030
## 2 2 1 1 1 1 1 1 1 1 2 2 1 1 1 1 2 1 1 1## Warning in ggplot2::guide_legend(overwrite.aes = list(size = plot_size)): Arguments in `...` must be used.
## ✖ Problematic argument:
## • overwrite.aes = list(size = plot_size)
## ℹ Did you misspell an argument name?## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative, notapplicable, positive
## Shapes are defined by PP1009, PP1011, PP1023, PP1024, PP1025, PP1026, PP1041, PP1042, PP1043, PP1044, PP2001, PP2005, PP2006, PP2007, PP2008, PP2019, PP2020, PP2021, PP2022, PP2030.
hs_hisat_nasal_nh_de <- all_pairwise(hs_hisat_nasal_nohealthy, filter = TRUE, model_svs = "svaseq",
model_fstring = "~ 0 + condition")## negative notapplicable positive
## 16 1 8## Removing 7481 low-count genes (14090 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 23963 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.
## This contrast put the denominator first.
## This contrast put the denominator first.## conditions
## negative notapplicable positive
## 16 1 8## calcNormFactors has been renamed to normLibSizes## conditions
## negative notapplicable positive
## 16 1 8## conditions
## negative notapplicable positive
## 16 1 8
## Error in NOISeq::noiseqbio(norm_input, k = k, norm = norm, factor = condition_column, :
## ERROR: To run NOISeqBIO at least two replicates per condition are needed.
## Please, run NOISeq if there are not enough replicates in your experiment.## Warning in correlate_de_tables(results, annot_df = annot_df, extra_contrasts = extra_contrasts): The noiseq results are not of class
## hpgltools::noiseq_pairwise.hs_hisat_nasal_nh_table <- combine_de_tables(
hs_hisat_nasal_nh_de, excel = "excel/hs_hisat_nasal_nh_table.xlsx")## Deleting the file excel/hs_hisat_nasal_nh_table.xlsx before writing the tables.## Looking for subscript invalid names, end of extract_keepers.Try removing the samples for which we have a duplicate/person across mRNA and Rz, removing only the Rz samples.
The cheesy easy way to do this is to just check for person PP1009, 1011, 2001 2020 && library_type = RZ, but it should be trivial to do it in a smarter way… But I will probably mess that up the first time and don’t want to be too annoying.
11.1 We have both library types for 4 people: 1009, 1011, 2001, and 2020.
excluded_duplicates <- subset_se(hs_hisat_nasal_nohealthy,
subset = "!(participant_code=='PP1009'&library_type=='RZ')") %>%
subset_se(subset = "!(participant_code=='PP1011'&library_type=='RZ')") %>%
subset_se(subset = "!(participant_code=='PP2001'&library_type=='RZ')") %>%
subset_se(subset = "!(participant_code=='PP2020'&library_type=='RZ')")
excluded_duplicates_norm <- normalize(excluded_duplicates, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 7658 low-count genes (13913 remaining).## transform_counts: Found 402 values equal to 0, adding 1 to the matrix.newnames <- colData(excluded_duplicates)[["participant_code"]]
pp(file = "images/excluded_duplicates_norm_pca.png",
image = plot_pca(excluded_duplicates_norm, exp_names = newnames, plot_labels = TRUE))## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable convergence failure
excluded_duplicates_nb <- normalize(excluded_duplicates, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 7658 low-count genes (13913 remaining).## transform_counts: Found 560 values less than 0.## transform_counts: Found 560 values equal to 0, adding 1 to the matrix.pp(file = "images/excluded_duplicates_nb_pca.png",
image = plot_pca(excluded_duplicates_nb, exp_names = newnames, plot_labels = TRUE))
excluded_duplicates_de <- all_pairwise(excluded_duplicates, filter = TRUE, model_svs = "svaseq",
model_fstring = "~ 0 + condition")## negative notapplicable positive
## 14 1 6## Removing 7658 low-count genes (13913 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 18021 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.
## This contrast put the denominator first.
## This contrast put the denominator first.## conditions
## negative notapplicable positive
## 14 1 6## calcNormFactors has been renamed to normLibSizes## conditions
## negative notapplicable positive
## 14 1 6## conditions
## negative notapplicable positive
## 14 1 6
## Error in NOISeq::noiseqbio(norm_input, k = k, norm = norm, factor = condition_column, :
## ERROR: To run NOISeqBIO at least two replicates per condition are needed.
## Please, run NOISeq if there are not enough replicates in your experiment.## Warning in correlate_de_tables(results, annot_df = annot_df, extra_contrasts = extra_contrasts): The noiseq results are not of class
## hpgltools::noiseq_pairwise.excluded_duplicates_table <- combine_de_tables(excluded_duplicates_de, excel = "excel/excluded_duplicates_table.xlsx")## Deleting the file excel/excluded_duplicates_table.xlsx before writing the tables.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 notapplicable_vs_negative 1 4 2 1 0 0
## 2 positive_vs_negative 132 30 112 17 88 10
## 3 positive_vs_notapplicable 4 1 2 1 0 0## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the UpSetR package.
## Please report the issue at <https://github.com/hms-dbmi/UpSetR/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the UpSetR package.
## Please report the issue at <https://github.com/hms-dbmi/UpSetR/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Plot describing unique/shared genes in a differential expression table.
excluded_duplicates_sig <- extract_significant_genes(excluded_duplicates_table, excel = "excel/excluded_duplicates_sig.xlsx")## Deleting the file excel/excluded_duplicates_sig.xlsx before writing the tables.## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## notapplicable_vs_negative 1 4
## positive_vs_negative 132 30
## positive_vs_notapplicable 4 1
excluded_up <- excluded_duplicates_sig[["deseq"]][["ups"]][["positive_vs_negative"]]
excluded_down <- excluded_duplicates_sig[["deseq"]][["downs"]][["positive_vs_negative"]]
excluded_up_gp <- simple_gprofiler(excluded_up)
excluded_up_gp
excluded_up_gp_bp_plots <- plot_enrichresult(excluded_up_gp[["BP_enrich"]])## Warning: showCategory (30) is larger than available items (25). Using 25## Warning: showCategory (30) is larger than available items (25). Using 25## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.## Warning: showCategory (30) is larger than available items (25). Using 25
## showCategory (30) is larger than available items (25). Using 25
excluded_down_gp <- simple_gprofiler(excluded_down)
excluded_down_gp
excluded_down_gp_bp_plots <- plot_enrichresult(excluded_down_gp[["BP_enrich"]])## There are insufficient significant results to plot.## Error in `pp()`:
## ! The image does not appear to exist.## Error in `pp()`:
## ! The image does not appear to exist.12 Normalize
A couple plots of all the salmon samples colored by sample type.
hs_salmon_norm <- normalize(hs_salmon, transform = "log2", convert = "cpm",
filter = TRUE, norm = "quant")## Removing 3015 low-count genes (7870 remaining).## transform_counts: Found 494278 values equal to 0, adding 1 to the matrix.hs_salmon_corheat <- plot_corheat(hs_salmon_norm)
pp(file = "images/hs_salmon_sampletype_pca.png", image = plot_pca(hs_salmon_norm))
hs_hisat_norm <- normalize(hs_hisat, transform = "log2", convert = "cpm",
filter = TRUE, norm = "quant")## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 21908 values equal to 0, adding 1 to the matrix.hs_hisat_corheat <- plot_corheat(hs_hisat_norm)
pp(file = "images/hs_hisat_sampletype_corheat.png")
hs_hisat_corheat## A heatmap of pairwise sample correlations ranging from:
## 0.371396107834044 to 0.990527166242753.## png
## 2## A heatmap of pairwise sample correlations ranging from:
## 0.371396107834044 to 0.990527166242753.
hs_hisat_pca <- plot_pca(hs_hisat_norm)
pp(file = "images/hs_hisat_sampletype_pca.png", image = plot_pca(hs_hisat_norm))
12.1 Remove samples without 7SL detection state
While we are at it, color by the 7SL detection and shape by sample type (Rz/mRNA).
In this instance I have separate colors for 7SL+ and 7SL- combined with celltype.
hs_hisat_detected <- subset_se(hs_hisat, subset = "detectionparasiteby7sl!='unknown'") %>%
subset_se(subset = "detectionparasiteby7sl!='notapplicable'") %>%
set_conditions(fact = "sample_7sl") %>%
set_batches("sample_type")## The numbers of samples by condition are:##
## nasalswab_negative nasalswab_positive pbmcs_negative pbmcs_positive
## 16 8 26 4
## skinbiopsynon-lesion_negative skinbiopsynon-lesion_positive skinbiopsyscar_negative wbcs_negative
## 26 1 21 24
## wbcs_positive
## 5## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 24 30 27 21 29Print a PCA of this using our generic normalization.
hs_hisat_detect_norm <- normalize(hs_hisat_detected, transform = "log2", convert = "cpm",
filter = TRUE, norm = "quant")## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 21908 values equal to 0, adding 1 to the matrix.hs_hisat_detect_pca <- plot_pca(hs_hisat_detect_norm)
pp(file = "images/hs_hisat_7sldetected_pca.png", image = hs_hisat_detect_pca, width = 18, height = 9)
Followed by the same samples treated with SVA.
hs_hisat_detect_nb <- normalize(hs_hisat_detected, transform = "log2", convert = "cpm",
filter = TRUE, batch = "svaseq")## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 36985 values less than 0.## transform_counts: Found 36985 values equal to 0, adding 1 to the matrix.pp(file = "images/hs_hisat_7sldetected_sva_pca.png",
image = plot_pca(hs_hisat_detect_nb), height = 9, width = 18)
## The numbers of samples by condition are:##
## negative positive
## 113 18## Setting colors with no pre-defined colors, using the Dark2 palette.hs_hisat_7sl_nb <- normalize(hs_hisat_only_7sl, transform = "log2", convert = "cpm",
batch = "svaseq", filter = TRUE)## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 22881 values less than 0.## transform_counts: Found 22881 values equal to 0, adding 1 to the matrix.pp(file = "images/hs_hisat_7sl_sva_pca.png", image = plot_pca(hs_hisat_7sl_nb),
height = 9, width = 18)
13 Compare distribution of RZ/Stranded libraries
Maria Adelaida is interested in the distribution of the relatively few rz samples vs the relatively large number of stranded mRNA libraries.
I think it is likely that the nasal samples are of primary interest.
Oh, it turns out the automatically generated colors for this are terrible. Maybe we do not care, there are so few 7SL positive mRNA samples that it is basically irrelevant.
hs_hisat_mrna_7sl <- set_conditions(hs_hisat_mrna, fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "sample_type")## The numbers of samples by condition are:##
## negative notapplicable positive
## 106 1 13## Setting colors with no pre-defined colors, using the Dark2 palette.
## The numbers of samples by condition are:##
## negative_mRNA positive_mRNA
## 106 13## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 15 30 25 20 29hs_hisat_mrna_7sl_norm <- normalize(hs_hisat_mrna_7sl, convert = "cpm", filter = TRUE,
norm = "quant", transform = "log2")## Removing 5874 low-count genes (15697 remaining).## transform_counts: Found 24749 values equal to 0, adding 1 to the matrix.## This still clusters primarily by sample type, and there are precious few positive samples.
plot_pca(hs_hisat_mrna_7sl_norm)## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_mRNA, positive_mRNA
## Shapes are defined by nasalswab, pbmcs, skinbiopsynon-lesion, skinbiopsyscar, wbcs.
Replot this following treatment with SVA. At least in this instance it is possible to differentiate the sample types in the blob.
hs_hisat_mrna_7sl_nb <- normalize(hs_hisat_mrna_7sl, convert = "cpm", filter = TRUE,
batch = "sva", transform = "log2")## Removing 5874 low-count genes (15697 remaining).## transform_counts: Found 15901 values less than 0.## transform_counts: Found 15901 values equal to 0, adding 1 to the matrix.hs_hisat_mrna_sva_pca <- plot_pca(hs_hisat_mrna_7sl_nb)
pp(file = "images/hs_hisat_mrna_sva_pca.png")
hs_hisat_mrna_sva_pca## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_mRNA, positive_mRNA
## Shapes are defined by nasalswab, pbmcs, skinbiopsynon-lesion, skinbiopsyscar, wbcs.## png
## 2## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_mRNA, positive_mRNA
## Shapes are defined by nasalswab, pbmcs, skinbiopsynon-lesion, skinbiopsyscar, wbcs.
13.1 Perform DESeq using these
## Error:
## ! unable to find an inherited method for function 'conditions' for signature 'object = "SummarizedExperiment"'hs_hisat_mrna_7sl_de <- all_pairwise(hs_hisat_mrna_7sl, model_fstring = "~ 0 + condition",
model_svs = "svaseq", filter = TRUE, force = TRUE)## negative_mRNA positive_mRNA
## 106 13## Removing 5874 low-count genes (15697 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 345302 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## 1 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_mRNA positive_mRNA
## 106 13## calcNormFactors has been renamed to normLibSizes## conditions
## negative_mRNA positive_mRNA
## 106 13## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_mRNA positive_mRNA
## 106 13## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## p_RNA___RN
## basic_vs_deseq 0.1490497
## basic_vs_dream 0.3170209
## basic_vs_ebseq 0.6061506
## basic_vs_edger 0.1915985
## basic_vs_limma 0.2866776
## basic_vs_noiseq 0.3221382
## deseq_vs_dream 0.3338728
## deseq_vs_ebseq 0.6026272
## deseq_vs_edger 0.3650626
## deseq_vs_limma 0.2847882
## deseq_vs_noiseq 0.3390230
## dream_vs_ebseq 0.4760589
## dream_vs_edger 0.7027551
## dream_vs_limma 0.9396614
## dream_vs_noiseq 0.3694242
## ebseq_vs_edger 0.4337269
## ebseq_vs_limma 0.4217813
## ebseq_vs_noiseq 0.6186556
## edger_vs_limma 0.6621643
## edger_vs_noiseq 0.4324803
## limma_vs_noiseq 0.5049929## Error in `pp()`:
## ! The image does not appear to exist.hs_hisat_mrna_7sl_table <- combine_de_tables(
hs_hisat_mrna_7sl_de,
excel = glue("excel/de_tables/hs_hisat_all_mrna_7sl_table-v{ver}.xlsx"))## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_mRNA_vs_negative_mRNA 7 55 5 0 2 1## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
pp(file = "images/sig_deseq_7sl_genes.png", image = hs_hisat_mrna_7sl_table[["plots"]][["positive_mRNA_vs_negative_mRNA"]][["deseq_vol_plots"]])
hs_hisat_mrna_7sl_sig <- extract_significant_genes(
hs_hisat_mrna_7sl_table,
excel = glue("excel/de_sig/hs_hisat_all_mrna_7sl_sig-v{ver}.xlsx"))
hs_hisat_mrna_7sl_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## positive_mRNA_vs_negative_mRNA 7 55
13.1.1 Salmon nasal samples, mRNA libraries
Now restrict to just the nasal samples.
hs_salmon_nasal_mrna_7sl <- subset_se(hs_salmon_mrna, subset = "sample_type=='nasalswab'") %>%
set_conditions(fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "participant_code")## The numbers of samples by condition are:##
## negative positive
## 9 2## Setting colors with no pre-defined colors, using the Dark2 palette.
## The numbers of samples by condition are:##
## negative_mRNA positive_mRNA
## 9 2## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## PP1009 PP1011 PP1023 PP1024 PP1025 PP1026 PP2001 PP2020 PP2021 PP2022 PP2030
## 1 1 1 1 1 1 1 1 1 1 1hs_hisat_nasal_mrna_7sl <- subset_se(hs_hisat_mrna, subset = "sample_type=='nasalswab'") %>%
set_conditions(fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "participant_code")## The numbers of samples by condition are:##
## negative notapplicable positive
## 12 1 3## Setting colors with no pre-defined colors, using the Dark2 palette.## The numbers of samples by condition are:##
## negative_mRNA positive_mRNA
## 12 3## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## PP1009 PP1011 PP1023 PP1024 PP1025 PP1026 PP1042 PP1043 PP1044 PP2001 PP2005 PP2020 PP2021 PP2022 PP2030
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1Print the normalized PCA showing only a couple of positive samples in this set.
Small note: I slightly changed how set_conditions works to drop unused levels.
hs_hisat_nasal_mrna_7sl_norm <- normalize(hs_hisat_nasal_mrna_7sl, convert = "cpm",
filter = TRUE, norm = "quant", transform = "log2")## Removing 8184 low-count genes (13387 remaining).## transform_counts: Found 43 values equal to 0, adding 1 to the matrix.## Warning in ggplot2::guide_legend(overwrite.aes = list(size = plot_size)): Arguments in `...` must be used.
## ✖ Problematic argument:
## • overwrite.aes = list(size = plot_size)
## ℹ Did you misspell an argument name?
hs_hisat_nasal_mrna_7sl_nb <- normalize(hs_hisat_nasal_mrna_7sl, convert = "cpm", filter = TRUE,
batch = "sva", transform = "log2")## Removing 8184 low-count genes (13387 remaining).## transform_counts: Found 230 values less than 0.## transform_counts: Found 230 values equal to 0, adding 1 to the matrix.## Warning in ggplot2::guide_legend(overwrite.aes = list(size = plot_size)): Arguments in `...` must be used.
## ✖ Problematic argument:
## • overwrite.aes = list(size = plot_size)
## ℹ Did you misspell an argument name?
13.1.2 Perform pairwise analysis of these
hs_hisat_nasal_mrna_7sl_de <- all_pairwise(hs_hisat_nasal_mrna_7sl, model_fstring = "~ 0 + condition",
model_svs = "svaseq", filter = TRUE, force = TRUE)## negative_mRNA positive_mRNA
## 12 3## Removing 8184 low-count genes (13387 remaining).## Warning in ggplot2::guide_legend(overwrite.aes = list(size = plot_size)): Arguments in `...` must be used.
## ✖ Problematic argument:
## • overwrite.aes = list(size = plot_size)
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • overwrite.aes = list(size = plot_size)
## ℹ Did you misspell an argument name?## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 6602 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_mRNA positive_mRNA
## 12 3## calcNormFactors has been renamed to normLibSizes## conditions
## negative_mRNA positive_mRNA
## 12 3## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_mRNA positive_mRNA
## 12 3## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## p_RNA___RN
## basic_vs_deseq 0.7084687
## basic_vs_dream 0.7909874
## basic_vs_ebseq 0.8097290
## basic_vs_edger 0.7826199
## basic_vs_limma 0.8107395
## basic_vs_noiseq 0.8636917
## deseq_vs_dream 0.8245755
## deseq_vs_ebseq 0.8685864
## deseq_vs_edger 0.9520459
## deseq_vs_limma 0.8229842
## deseq_vs_noiseq 0.8390690
## dream_vs_ebseq 0.7468057
## dream_vs_edger 0.9071112
## dream_vs_limma 0.9775754
## dream_vs_noiseq 0.7779064
## ebseq_vs_edger 0.8794028
## ebseq_vs_limma 0.7628400
## ebseq_vs_noiseq 0.9820948
## edger_vs_limma 0.9046415
## edger_vs_noiseq 0.8764531
## limma_vs_noiseq 0.7950424hs_hisat_nasal_mrna_7sl_table <- combine_de_tables(
hs_hisat_nasal_mrna_7sl_de,
excel = glue("excel/de_tables/hs_hisat_nasal_mrna_7sl_table-v{ver}.xlsx"))## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_mRNA_vs_negative_mRNA 9 1 13 0 3 0## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
hs_hisat_nasal_mrna_7sl_sig <- extract_significant_genes(
hs_hisat_nasal_mrna_7sl_table,
excel = glue("excel/de_sig/hs_hisat_nasal_mrna_7sl_sig-v{ver}.xlsx"))
hs_hisat_nasal_mrna_7sl_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## positive_mRNA_vs_negative_mRNA 9 1
13.2 Repeat with the ribo zero samples
13.2.1 7SL samples, ribozero
Extract the ribozero samples into a new datastructure.
hs_hisat_rz_7sl <- set_conditions(hs_hisat_rz, fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "sample_type")## The numbers of samples by condition are:##
## negative positive
## 7 5## Setting colors with no pre-defined colors, using the Dark2 palette.
## The numbers of samples by condition are:##
## negative_RZ positive_RZ
## 7 5## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## nasalswab skinbiopsynon-lesion skinbiopsyscar
## 9 2 1hs_salmon_rz_7sl <- set_conditions(hs_salmon_rz, fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "sample_type")## The numbers of samples by condition are:##
## negative positive
## 7 5## Setting colors with no pre-defined colors, using the Dark2 palette.## The numbers of samples by condition are:##
## negative_RZ positive_RZ
## 7 5## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## nasalswab skinbiopsynon-lesion skinbiopsyscar
## 9 2 1and play with them.
hs_hisat_rz_7sl_norm <- normalize(hs_hisat_rz_7sl, convert = "cpm", filter = TRUE,
norm = "quant", transform = "log2")## Removing 7058 low-count genes (14513 remaining).## transform_counts: Found 317 values equal to 0, adding 1 to the matrix.## This still clusters primarily by sample type, and there are precious few positive samples.
hs_hisat_rz_7sl_norm_pca <- plot_pca(hs_hisat_rz_7sl_norm)
pp(file = "imaegs/hs_hisat_rz_7sl_norm_pca.png",
image = hs_hisat_rz_7sl_norm_pca)
hs_salmon_rz_7sl_norm <- normalize(hs_salmon_rz_7sl, convert = "cpm", filter = TRUE,
norm = "quant", transform = "log2")## Removing 5861 low-count genes (5024 remaining).## transform_counts: Found 25004 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_RZ, positive_RZ
## Shapes are defined by nasalswab, skinbiopsynon-lesion, skinbiopsyscar.
hs_hisat_rz_7sl_de <- all_pairwise(hs_hisat_rz_7sl, model_fstring = "~ 0 + condition",
model_svs = "svaseq", filter = TRUE, force = TRUE)## negative_RZ positive_RZ
## 7 5## Removing 7058 low-count genes (14513 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 13470 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_RZ positive_RZ
## 7 5## calcNormFactors has been renamed to normLibSizes## conditions
## negative_RZ positive_RZ
## 7 5## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_RZ positive_RZ
## 7 5## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## ps_RZ___RZ
## basic_vs_deseq 0.4975228
## basic_vs_dream 0.5059912
## basic_vs_ebseq 0.7284212
## basic_vs_edger 0.4713859
## basic_vs_limma 0.5216288
## basic_vs_noiseq 0.4452867
## deseq_vs_dream 0.9303126
## deseq_vs_ebseq 0.4567858
## deseq_vs_edger 0.9475635
## deseq_vs_limma 0.9274922
## deseq_vs_noiseq 0.7956467
## dream_vs_ebseq 0.4494630
## dream_vs_edger 0.8721083
## dream_vs_limma 0.9931848
## dream_vs_noiseq 0.8420597
## ebseq_vs_edger 0.4884993
## ebseq_vs_limma 0.4257163
## ebseq_vs_noiseq 0.5017234
## edger_vs_limma 0.8665323
## edger_vs_noiseq 0.7561021
## limma_vs_noiseq 0.8195530hs_hisat_rz_7sl_table <- combine_de_tables(
hs_hisat_rz_7sl_de,
excel = glue("excel/de_tables/hs_hisat_rz_7sl_table-v{ver}.xlsx"))## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_RZ_vs_negative_RZ 44 22 64 34 0 0## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
hs_hisat_rz_7sl_sig <- extract_significant_genes(
hs_hisat_rz_7sl_table,
excel = glue("excel/de_sig/hs_hisat_rz_7sl_sig-v{ver}.xlsx"))
hs_hisat_rz_7sl_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## positive_RZ_vs_negative_RZ 44 22
13.2.2 Salmon 7SL samples, ribozero
hs_salmon_rz_7sl_de <- all_pairwise(hs_salmon_rz_7sl, model_fstring = "~ 0 + condition",
model_svs = "svaseq", filter = TRUE, force = TRUE)## negative_RZ positive_RZ
## 7 5## Removing 5861 low-count genes (5024 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 26490 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_RZ positive_RZ
## 7 5## calcNormFactors has been renamed to normLibSizes## conditions
## negative_RZ positive_RZ
## 7 5## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_RZ positive_RZ
## 7 5## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## ps_RZ___RZ
## basic_vs_deseq 0.4238662
## basic_vs_dream 0.6337968
## basic_vs_ebseq 0.6778526
## basic_vs_edger 0.5149572
## basic_vs_limma 0.6094502
## basic_vs_noiseq 0.6304777
## deseq_vs_dream 0.6678288
## deseq_vs_ebseq 0.4592409
## deseq_vs_edger 0.8448381
## deseq_vs_limma 0.6170263
## deseq_vs_noiseq 0.6359009
## dream_vs_ebseq 0.5011153
## dream_vs_edger 0.8165980
## dream_vs_limma 0.9644507
## dream_vs_noiseq 0.7516089
## ebseq_vs_edger 0.5631190
## ebseq_vs_limma 0.4599039
## ebseq_vs_noiseq 0.7097083
## edger_vs_limma 0.7557171
## edger_vs_noiseq 0.7517053
## limma_vs_noiseq 0.7346500hs_salmon_rz_7sl_table <- combine_de_tables(
hs_salmon_rz_7sl_de,
excel = glue("excel/de_tables/hs_salmon_rz_7sl_table-v{ver}.xlsx"))## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_RZ_vs_negative_RZ 29 42 42 34 7 2## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
hs_salmon_rz_7sl_sig <- extract_significant_genes(
hs_salmon_rz_7sl_table,
excel = glue("excel/de_sig/hs_salmon_rz_7sl_sig-v{ver}.xlsx"))
hs_salmon_rz_7sl_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## positive_RZ_vs_negative_RZ 29 42
13.2.3 7SL nasal samples, ribozero
Now restrict to just the nasal samples.
hs_hisat_nasal_rz_7sl <- subset_se(hs_hisat_rz_7sl, subset = "sample_type=='nasalswab'") %>%
set_conditions(fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "sample_type")## The numbers of samples by condition are:##
## negative positive
## 4 5## Setting colors with no pre-defined colors, using the Dark2 palette.
## The numbers of samples by condition are:##
## negative_RZ positive_RZ
## 4 5## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## nasalswab
## 9hs_hisat_nasal_rz_7sl_norm <- normalize(hs_hisat_nasal_rz_7sl, convert = "cpm", filter = TRUE,
norm = "quant", transform = "log2")## Removing 8088 low-count genes (13483 remaining).## transform_counts: Found 11 values equal to 0, adding 1 to the matrix.hs_hisat_nasal_rz_7sl_npca <- plot_pca(hs_hisat_nasal_rz_7sl_norm)
pp(file = "images/hs_hisat_nasal_rz_7sl_npca.png")
hs_hisat_nasal_rz_7sl_npca[["plot"]]
dev.off()## png
## 2## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_RZ, positive_RZ
## Shapes are defined by nasalswab.
hs_hisat_nasal_rz_7sl_nb <- normalize(hs_hisat_nasal_rz_7sl, convert = "cpm", filter = TRUE,
batch = "sva", transform = "log2")## Removing 8088 low-count genes (13483 remaining).## transform_counts: Found 174 values less than 0.## transform_counts: Found 174 values equal to 0, adding 1 to the matrix.hs_hisat_nasal_rz_7sl_nbpca <- plot_pca(hs_hisat_nasal_rz_7sl_nb)
pp(file = "images/hs_hisat_nasal_rz_7sl_nbpca.png")
hs_hisat_nasal_rz_7sl_nbpca[["plot"]]
dev.off()## png
## 2## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_RZ, positive_RZ
## Shapes are defined by nasalswab.
hs_salmon_nasal_rz_7sl <- subset_se(hs_salmon_rz_7sl, subset = "sample_type=='nasalswab'") %>%
set_conditions(fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "sample_type")## The numbers of samples by condition are:##
## negative positive
## 4 5## Setting colors with no pre-defined colors, using the Dark2 palette.
## The numbers of samples by condition are:##
## negative_RZ positive_RZ
## 4 5## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## nasalswab
## 9hs_salmon_nasal_rz_7sl_norm <- normalize(hs_salmon_nasal_rz_7sl, convert = "cpm", filter = TRUE,
norm = "quant", transform = "log2")## Removing 6872 low-count genes (4013 remaining).## transform_counts: Found 11896 values equal to 0, adding 1 to the matrix.hs_salmon_nasal_rz_7sl_npca <- plot_pca(hs_salmon_nasal_rz_7sl_norm)
pp(file = "images/hs_salmon_nasal_rz_7sl_npca.png")
hs_salmon_nasal_rz_7sl_npca[["plot"]]
dev.off()## png
## 2## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_RZ, positive_RZ
## Shapes are defined by nasalswab.
hs_salmon_nasal_rz_7sl_nb <- normalize(hs_salmon_nasal_rz_7sl, convert = "cpm", filter = TRUE,
batch = "sva", transform = "log2")## Removing 6872 low-count genes (4013 remaining).## transform_counts: Found 3848 values less than 0.## transform_counts: Found 3848 values equal to 0, adding 1 to the matrix.hs_salmon_nasal_rz_7sl_nbpca <- plot_pca(hs_salmon_nasal_rz_7sl_nb)
pp(file = "images/hs_salmon_nasal_rz_7sl_nbpca.png")
hs_salmon_nasal_rz_7sl_nbpca[["plot"]]
dev.off()## png
## 2## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_RZ, positive_RZ
## Shapes are defined by nasalswab.
13.2.3.1 DE of this ever shrinking set
hs_hisat_nasal_rz_7sl_de <- all_pairwise(hs_hisat_nasal_rz_7sl, model_fstring = "~ 0 + condition",
model_svs = "svaseq", filter = TRUE, force = TRUE)## negative_RZ positive_RZ
## 4 5## Removing 8088 low-count genes (13483 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 3677 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_RZ positive_RZ
## 4 5## calcNormFactors has been renamed to normLibSizes## conditions
## negative_RZ positive_RZ
## 4 5## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_RZ positive_RZ
## 4 5## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## ps_RZ___RZ
## basic_vs_deseq 0.9409616
## basic_vs_dream 0.9393628
## basic_vs_ebseq 0.8627583
## basic_vs_edger 0.9430131
## basic_vs_limma 0.9344504
## basic_vs_noiseq 0.8672976
## deseq_vs_dream 0.9263685
## deseq_vs_ebseq 0.8455327
## deseq_vs_edger 0.9990189
## deseq_vs_limma 0.9245912
## deseq_vs_noiseq 0.8505229
## dream_vs_ebseq 0.8927150
## dream_vs_edger 0.9297926
## dream_vs_limma 0.9919308
## dream_vs_noiseq 0.8992863
## ebseq_vs_edger 0.8514828
## ebseq_vs_limma 0.8695064
## ebseq_vs_noiseq 0.9993307
## edger_vs_limma 0.9278476
## edger_vs_noiseq 0.8564436
## limma_vs_noiseq 0.8777438hs_hisat_nasal_rz_7sl_table <- combine_de_tables(
hs_hisat_nasal_rz_7sl_de,
excel = glue("excel/de_tables/hs_hisat_nasal_rz_7sl_table-v{ver}.xlsx"))## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_RZ_vs_negative_RZ 11 4 31 7 0 1## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
hs_hisat_nasal_rz_7sl_sig <- extract_significant_genes(
hs_hisat_nasal_rz_7sl_table,
excel = glue("excel/de_sig/hs_hisat_nasal_rz_7sl_sig-v{ver}.xlsx"))
hs_hisat_nasal_rz_7sl_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## positive_RZ_vs_negative_RZ 11 4
hs_salmon_nasal_rz_7sl_de <- all_pairwise(hs_salmon_nasal_rz_7sl, model_fstring = "~ 0 + condition",
model_svs = "svaseq", filter = TRUE, force = TRUE)## negative_RZ positive_RZ
## 4 5## Removing 6872 low-count genes (4013 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 12728 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_RZ positive_RZ
## 4 5## calcNormFactors has been renamed to normLibSizes## conditions
## negative_RZ positive_RZ
## 4 5## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_RZ positive_RZ
## 4 5## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## ps_RZ___RZ
## basic_vs_deseq 0.6382512
## basic_vs_dream 0.8538690
## basic_vs_ebseq 0.7075406
## basic_vs_edger 0.7398921
## basic_vs_limma 0.8785550
## basic_vs_noiseq 0.8325153
## deseq_vs_dream 0.7227097
## deseq_vs_ebseq 0.6190429
## deseq_vs_edger 0.8955159
## deseq_vs_limma 0.6933449
## deseq_vs_noiseq 0.6411391
## dream_vs_ebseq 0.7020063
## dream_vs_edger 0.8299055
## dream_vs_limma 0.9754756
## dream_vs_noiseq 0.7862041
## ebseq_vs_edger 0.7750562
## ebseq_vs_limma 0.7102171
## ebseq_vs_noiseq 0.9350743
## edger_vs_limma 0.8004156
## edger_vs_noiseq 0.7817831
## limma_vs_noiseq 0.7978367hs_salmon_nasal_rz_7sl_table <- combine_de_tables(
hs_salmon_nasal_rz_7sl_de,
excel = glue("excel/de_tables/hs_salmon_nasal_rz_7sl_table-v{ver}.xlsx"))## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_RZ_vs_negative_RZ 8 35 23 67 4 0## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
hs_salmon_nasal_rz_7sl_sig <- extract_significant_genes(
hs_salmon_nasal_rz_7sl_table,
excel = glue("excel/de_sig/hs_salmon_nasal_rz_7sl_sig-v{ver}.xlsx"))
hs_salmon_nasal_rz_7sl_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## positive_RZ_vs_negative_RZ 8 35
13.3 Compare 7SL status for the PBMC samples
hs_hisat_pbmc_7sl <- subset_se(hs_hisat, subset = "sample_type=='pbmcs'") %>%
set_conditions(fact = "detectionparasiteby7sl")## The numbers of samples by condition are:##
## negative positive
## 26 4## Setting colors with no pre-defined colors, using the Dark2 palette.hs_hisat_pbmc_mrna_7sl_norm <- normalize(hs_hisat_pbmc_7sl, transform = "log2",
convert = "cpm", norm = "quant",
filter = TRUE)## Removing 9085 low-count genes (12486 remaining).## transform_counts: Found 29 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative, positive
## Shapes are defined by mRNA.
13.4 Extract the blood cells (not PBMC)
13.4.1 Hisat
hs_hisat_wbc_mrna_7sl <- subset_se(hs_hisat_mrna, subset = "sample_type=='wbcs'") %>%
set_conditions(fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "sample_type")## The numbers of samples by condition are:##
## negative positive
## 24 5## Setting colors with no pre-defined colors, using the Dark2 palette.
## The numbers of samples by condition are:##
## negative_mRNA positive_mRNA
## 24 5## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## wbcs
## 29hs_hisat_wbc_mrna_7sl_norm <- normalize(hs_hisat_wbc_mrna_7sl, transform = "log2",
convert = "cpm", norm = "quant",
filter = TRUE)## Removing 9451 low-count genes (12120 remaining).## transform_counts: Found 37 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_mRNA, positive_mRNA
## Shapes are defined by wbcs.
hs_hisat_wbc_mrna_7sl_nb <- normalize(hs_hisat_wbc_mrna_7sl, transform = "log2",
convert = "cpm", batch = "svaseq",
filter = TRUE)## Removing 9451 low-count genes (12120 remaining).## transform_counts: Found 327 values less than 0.## transform_counts: Found 327 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_mRNA, positive_mRNA
## Shapes are defined by wbcs.
## png
## 213.4.2 Salmon
hs_salmon_wbc_mrna_7sl <- subset_se(hs_salmon_mrna, subset = "sample_type=='wbcs'") %>%
set_conditions(fact = "detectionparasiteby7sl") %>%
subset_se(subset = "condition!='notapplicable'") %>%
set_conditions(fact = "detection_type") %>%
set_batches(fact = "sample_type")## The numbers of samples by condition are:##
## negative positive
## 19 4## Setting colors with no pre-defined colors, using the Dark2 palette.
## The numbers of samples by condition are:##
## negative_mRNA positive_mRNA
## 19 4## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## wbcs
## 23hs_salmon_wbc_mrna_7sl_norm <- normalize(hs_salmon_wbc_mrna_7sl, transform = "log2",
convert = "cpm", norm = "quant",
filter = TRUE)## Removing 5882 low-count genes (5003 remaining).## transform_counts: Found 46809 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_mRNA, positive_mRNA
## Shapes are defined by wbcs.
hs_salmon_wbc_mrna_7sl_nb <- normalize(hs_salmon_wbc_mrna_7sl, transform = "log2",
convert = "cpm", batch = "svaseq",
filter = TRUE)## Removing 5882 low-count genes (5003 remaining).## transform_counts: Found 15805 values less than 0.## transform_counts: Found 15805 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative_mRNA, positive_mRNA
## Shapes are defined by wbcs.
hs_salmon_wbc_mrna_7sl_de <- all_pairwise(hs_salmon_wbc_mrna_7sl, model_fstring = "~ 0 + condition",
model_svs = "svaseq", filter = TRUE, force = TRUE)## negative_mRNA positive_mRNA
## 19 4## Removing 5882 low-count genes (5003 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 48484 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_mRNA positive_mRNA
## 19 4## calcNormFactors has been renamed to normLibSizes## conditions
## negative_mRNA positive_mRNA
## 19 4## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative_mRNA positive_mRNA
## 19 4## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## p_RNA___RN
## basic_vs_deseq 0.4576421
## basic_vs_dream 0.9115531
## basic_vs_ebseq 0.5885339
## basic_vs_edger 0.7318327
## basic_vs_limma 0.9051631
## basic_vs_noiseq 0.7614593
## deseq_vs_dream 0.4508623
## deseq_vs_ebseq 0.6529414
## deseq_vs_edger 0.7140047
## deseq_vs_limma 0.4373145
## deseq_vs_noiseq 0.6661746
## dream_vs_ebseq 0.5853173
## dream_vs_edger 0.7371504
## dream_vs_limma 0.9902388
## dream_vs_noiseq 0.7291375
## ebseq_vs_edger 0.8002916
## ebseq_vs_limma 0.5627182
## ebseq_vs_noiseq 0.9018166
## edger_vs_limma 0.7231412
## edger_vs_noiseq 0.8631201
## limma_vs_noiseq 0.7114644hs_salmon_wbc_mrna_7sl_table <- combine_de_tables(
hs_salmon_wbc_mrna_7sl_de,
excel = glue("excel/de_tables/hs_salmon_wbc_mrna_7sl_table-v{ver}.xlsx"))## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_mRNA_vs_negative_mRNA 41 203 22 15 7 3## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
hs_salmon_wbc_mrna_7sl_sig <- extract_significant_genes(
hs_salmon_wbc_mrna_7sl_table,
excel = glue("excel/sig_tables/hs_salmon_wbc_mrna_7sl_sig-v{ver}.xlsx"))
hs_salmon_wbc_mrna_7sl_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## positive_mRNA_vs_negative_mRNA 41 203
14 Healthy vs Scar samples
One query from our last meeting which I forgot about until I reread my TODO notes: compare the samples marked as healthy compared to those marked as scar. These are two distantly separate skin biopsies of the same person.
hs_hisat_skin_mrna_7sl <- subset_se(hs_hisat_mrna, subset = "hs=='scar'|hs=='healthy'")
hs_hisat_scarvs_mrna <- set_conditions(hs_hisat_skin_mrna_7sl, fact = "hs")## The numbers of samples by condition are:##
## healthy scar
## 25 20## Setting colors with no pre-defined colors, using the Dark2 palette.hs_hisat_scarvs_de <- all_pairwise(hs_hisat_scarvs_mrna, filter = TRUE,
model_svs = "svaseq", model_fstring = "~ 0 + condition")## healthy scar
## 25 20## Removing 7367 low-count genes (14204 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 35875 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## conditions
## healthy scar
## 25 20## calcNormFactors has been renamed to normLibSizes## conditions
## healthy scar
## 25 20## conditions
## healthy scar
## 25 20## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## scr_vs_hlt
## basic_vs_deseq 0.7220332
## basic_vs_dream 0.8350298
## basic_vs_ebseq 0.7706083
## basic_vs_edger 0.7381484
## basic_vs_limma 0.8370038
## basic_vs_noiseq 0.8764495
## deseq_vs_dream 0.9390006
## deseq_vs_ebseq 0.9348352
## deseq_vs_edger 0.9973377
## deseq_vs_limma 0.9348631
## deseq_vs_noiseq 0.9114047
## dream_vs_ebseq 0.9067304
## dream_vs_edger 0.9481686
## dream_vs_limma 0.9947109
## dream_vs_noiseq 0.9309838
## ebseq_vs_edger 0.9445094
## ebseq_vs_limma 0.9004439
## ebseq_vs_noiseq 0.9641659
## edger_vs_limma 0.9438002
## edger_vs_noiseq 0.9207857
## limma_vs_noiseq 0.9279679hs_hisat_scarvs_table <- combine_de_tables(
hs_hisat_scarvs_de,
excel = glue("excel/de_tables/hs_hisat_scarvs_mrna_table-v{ver}.xlsx"))## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 scar_vs_healthy 127 132 125 125 105 59## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
pp(file = "images/hisat_scarvs_deseq_genes.png", image = hs_hisat_scarvs_table[["plots"]][["scar_vs_nonlesion"]][["deseq_vol_plots"]])## Error in `pp()`:
## ! The image does not appear to exist.hs_hisat_scarvs_sig <- extract_significant_genes(
hs_hisat_scarvs_table,
excel = glue("excel/sig_tables/hs_hisat_scarvs_mrna_sig-v{ver}.xlsx"))
hs_hisat_scarvs_sig## A set of genes deemed significant according to deseq.## The parameters defining significant were:## LFC cutoff: 1 adj P cutoff: 0.05## deseq_up deseq_down
## scar_vs_healthy 127 132
15 Take a peek at the kraken results
hs_kraken_viral <- create_se(pre_meta[["new_meta"]], file_column = "kraken_matrix_viral",
handle_na = "zero")## Reading the sample metadata.## Checking the state of the condition column.## Checking the state of the batch column.## Checking the condition factor.## The sample definitions comprises: 206 rows(samples) and 200 columns(metadata fields).## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0002/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0009/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0010/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0011/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0018/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0019/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0020/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0012/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0013/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0014/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0021/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0022/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0023/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0015/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0016/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0017/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0024/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0025/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0026/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0038/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0006/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0007/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0008/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0005/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0004/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0015/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0016/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0017/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0018/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0019/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0020/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0021/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0022/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0023/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0001/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0002/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0003/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0004/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0005/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0006/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0007/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0008/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0009/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0010/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0011/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0012/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0013/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0014/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0003/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0027/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0028/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0029/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0030/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0031/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0032/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0035/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0033/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0034/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0036/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0037/outputs/20250918kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0039/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0040/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0041/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0042/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0043/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0044/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0045/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0046/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0047/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0048/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0049/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0050/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0051/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0052/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0053/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0054/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0055/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0056/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0057/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0058/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0059/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0060/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0061/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0062/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0063/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0064/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0065/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0066/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0067/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0068/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0069/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0070/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0071/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0072/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0073/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0074/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0075/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0076/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0077/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0078/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0079/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0080/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0081/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0082/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0083/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0084/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0085/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0086/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0087/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0088/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0089/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0090/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0091/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0092/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0093/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0094/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0095/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0096/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0097/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0098/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0099/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0100/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0101/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0102/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0103/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0104/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0106/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0116/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0113/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0112/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0024/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0025/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0026/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0027/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0028/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0108/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0115/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0114/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0110/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0107/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0109/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0105/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0111/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0117/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0118/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0123/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0124/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0031/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0032/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0033/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0034/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0035/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0036/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0037/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0038/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0039/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0040/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0044/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0125/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0126/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0127/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0128/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0129/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0130/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0131/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0132/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0119/outputs/11kraken_viral/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in create_se(pre_meta[["new_meta"]], file_column = "kraken_matrix_viral", : Some samples were removed when cross referencing the
## samples against the count data.## Warning in create_se(pre_meta[["new_meta"]], file_column = "kraken_matrix_viral", : There are some NAs in this data, the 'handle_nas'
## parameter may be required.## Matched 574 annotations and counts.## Saving the summarized experiment to 'se.rda'.## The final summarized experiment has 574 rows and 200 columns.hs_kraken_viral <- set_conditions(hs_kraken_viral, fact = "sample_type") %>%
set_batches("detectionparasiteby7sl")## The numbers of samples by condition are:##
## nasal swab Nasal Swab par parasite PBMCs skin biopsy non-lesion
## 20 5 11 28 29 26
## skin biopsy scar WBCs
## 21 28## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## negative positive unknown
## 110 18 40## Removing 0 low-count genes (574 remaining).## Did not recognize the normalization, leaving the table alone.
## Recognized normalizations include: 'qsmooth', 'sf', 'sf2', 'vsd', 'quant',
## 'tmm', 'qsmooth_median', 'upperquartile', and 'rle.'## transform_counts: Found 89608 values equal to 0, adding 1 to the matrix.## A heatmap of pairwise sample correlations ranging from:
## 0.14944097277619 to 0.973957039533747.
## A heatmap of pairwise sample distances ranging from:
## 5.77516038127018 to 40.2472163754132.
## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by nasal swab, Nasal Swab, par, parasite, PBMCs, skin biopsy non-lesion, skin biopsy scar, WBCs
## Shapes are defined by negative, positive, unknown.
nasal_kraken_viral <- subset_se(hs_kraken_viral, subset = "condition=='nasal swab'")
nasal_norm <- normalize(nasal_kraken_viral, filter = TRUE, norm = "cpm", transform = "log2")## Removing 0 low-count genes (574 remaining).## Did not recognize the normalization, leaving the table alone.
## Recognized normalizations include: 'qsmooth', 'sf', 'sf2', 'vsd', 'quant',
## 'tmm', 'qsmooth_median', 'upperquartile', and 'rle.'## transform_counts: Found 10544 values equal to 0, adding 1 to the matrix.## A heatmap of pairwise sample correlations ranging from:
## 0.767642773612499 to 0.959202277096667.
hs_kraken_bacteria <- create_se(pre_meta[["new_meta"]], file_column = "kraken_matrix_bacterial",
handle_na = "zero")## Reading the sample metadata.## Checking the state of the condition column.## Checking the state of the batch column.## Checking the condition factor.## The sample definitions comprises: 206 rows(samples) and 200 columns(metadata fields).## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0002/outputs/02kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0009/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0010/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0011/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0018/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0019/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0020/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0012/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0013/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0014/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0021/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0022/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0023/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0015/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0016/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0017/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0024/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0025/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0026/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0038/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0006/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0007/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0008/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0005/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0004/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0015/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0016/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0017/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0018/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0019/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0020/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0021/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0022/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0023/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0001/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0002/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0003/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0004/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0005/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0006/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0007/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0008/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0009/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0010/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0011/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0012/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0013/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0014/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0003/outputs/02kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0027/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0028/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0029/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0030/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0031/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0032/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0035/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0033/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0034/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0036/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0037/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0001/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0002/outputs/03kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0003/outputs/02kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0004/outputs/02kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0005/outputs/03kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0006/outputs/03kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0007/outputs/02kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0009/outputs/02kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0010/outputs/03kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0011/outputs/03kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0012/outputs/02kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0008/outputs/03kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0015/outputs/02kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0017/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0018/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0019/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0020/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0021/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0022/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0023/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0024/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0025/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0026/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRSL0027/outputs/06kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0039/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0040/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0041/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0042/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0043/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0044/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0045/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0046/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0047/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0048/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0049/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0050/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0051/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0052/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0053/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0054/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0055/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0056/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0057/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0058/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0059/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0060/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0061/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0062/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0063/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0064/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0065/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0066/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0067/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0068/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0069/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0070/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0071/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0072/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0073/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0074/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0075/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0076/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0077/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0078/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0079/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0080/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0081/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0082/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0083/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0084/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0085/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0086/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0087/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0088/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0089/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0090/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0091/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0092/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0093/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0094/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0095/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0096/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0097/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0101/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0102/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0116/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0108/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0115/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0114/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0110/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0107/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0109/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0105/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0111/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0117/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0118/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0123/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0124/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRCS0031/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0126/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
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## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0131/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0132/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in first_rownames != current_rownames: longer object length is not a multiple of shorter object length## Warning in read_counts(ids, files, countdir = countdir, file_type = file_type, : The file:
## /mnt/iscsi/scratch/atb/rnaseq/lpanamensis_persistence_2023/preprocessing/PRHU0119/outputs/11kraken_bacteria/kraken_report_matrix.tsv has
## mismatched rownames.## Warning in create_se(pre_meta[["new_meta"]], file_column = "kraken_matrix_bacterial", : Some samples were removed when cross referencing the
## samples against the count data.## Warning in create_se(pre_meta[["new_meta"]], file_column = "kraken_matrix_bacterial", : There are some NAs in this data, the 'handle_nas'
## parameter may be required.## Matched 1767 annotations and counts.## Saving the summarized experiment to 'se.rda'.## The final summarized experiment has 1767 rows and 200 columns.hs_kraken_bacteria <- set_conditions(hs_kraken_bacteria, fact = "sample_type") %>%
set_batches("detectionparasiteby7sl")## The numbers of samples by condition are:##
## lesion swab macrophages nasal swab Nasal Swab par parasite
## 5 13 21 5 11 30
## PBMCs skin biopsy non-lesion skin biopsy scar WBCs
## 29 27 22 29## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## negative positive unknown
## 111 20 61kraken_bacteria_norm <- normalize(hs_kraken_bacteria, filter = TRUE,
norm = "cpm", transform = "log2")## Removing 0 low-count genes (1767 remaining).## Did not recognize the normalization, leaving the table alone.
## Recognized normalizations include: 'qsmooth', 'sf', 'sf2', 'vsd', 'quant',
## 'tmm', 'qsmooth_median', 'upperquartile', and 'rle.'## transform_counts: Found 177516 values equal to 0, adding 1 to the matrix.## A heatmap of pairwise sample correlations ranging from:
## 0.349710698232674 to 0.964450742643494.
## A heatmap of pairwise sample distances ranging from:
## 32.6551513666332 to 162.969109084411.
## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by lesion swab, macrophages, nasal swab, Nasal Swab, par, parasite, PBMCs, skin biopsy non-lesion, skin biopsy scar, WBCs
## Shapes are defined by negative, positive, unknown.
16 Nasal as a proxy for everything else
In the beginning of this document, I created a peculiar factor out of the nasal sample 7SL state and applied its result to every other sample for each person; thus a person who was positive for the nasal sample was deemed positive for everything. Let us see what that looks like…
## The numbers of samples by condition are:##
## negative positive
## 84 19## Setting colors with no pre-defined colors, using the Dark2 palette.## The numbers of samples by condition are:##
## negative positive
## 103 29## Setting colors with no pre-defined colors, using the Dark2 palette.nasal_7sl_salmon_se_nb <- normalize(nasal_7sl_salmon_se, transform = "log2", convert = "cpm",
filter = TRUE, batch = "svaseq")## Removing 3015 low-count genes (7870 remaining).## transform_counts: Found 161726 values less than 0.## transform_counts: Found 161726 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative, positive
## Shapes are defined by mRNA, RZ.
nasal_7sl_hisat_se_nb <- normalize(nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
filter = TRUE, batch = "svaseq")## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 20773 values less than 0.## transform_counts: Found 20773 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative, positive
## Shapes are defined by mRNA, RZ.
nasal_7sl_salmon_de <- all_pairwise(nasal_7sl_salmon_se, filter = TRUE, force = TRUE,
model_svs = "svaseq", model_fstring = "~ 0 + condition")## negative positive
## 84 19## Removing 3015 low-count genes (7870 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 519317 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative positive
## 84 19## calcNormFactors has been renamed to normLibSizes## conditions
## negative positive
## 84 19## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative positive
## 84 19## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## pstv_vs_ng
## basic_vs_deseq 0.2155639
## basic_vs_dream 0.5394501
## basic_vs_ebseq 0.4294889
## basic_vs_edger 0.4129502
## basic_vs_limma 0.4958869
## basic_vs_noiseq 0.5870426
## deseq_vs_dream 0.3296758
## deseq_vs_ebseq 0.6054542
## deseq_vs_edger 0.5827008
## deseq_vs_limma 0.2599123
## deseq_vs_noiseq 0.4783114
## dream_vs_ebseq 0.4035646
## dream_vs_edger 0.6802855
## dream_vs_limma 0.9071873
## dream_vs_noiseq 0.5494830
## ebseq_vs_edger 0.6304198
## ebseq_vs_limma 0.3208948
## ebseq_vs_noiseq 0.7107129
## edger_vs_limma 0.5845931
## edger_vs_noiseq 0.6616207
## limma_vs_noiseq 0.4870543nasal_7sl_hisat_de <- all_pairwise(nasal_7sl_hisat_se, filter = TRUE,
model_svs = "svaseq", model_fstring = "~ 0 + condition")## negative positive
## 103 29## Removing 5557 low-count genes (16014 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 408837 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## conditions
## negative positive
## 103 29## calcNormFactors has been renamed to normLibSizes## conditions
## negative positive
## 103 29## conditions
## negative positive
## 103 29## A pairwise differential expression with results from: basic, deseq, ebseq, edger, limma, noiseq.## This used a surrogate/batch estimate from: svaseq.## The primary analysis performed 1 comparisons.## The logFC agreement among the methods follows:## pstv_vs_ng
## basic_vs_deseq 0.4026190
## basic_vs_dream 0.5423231
## basic_vs_ebseq 0.7400646
## basic_vs_edger 0.5238615
## basic_vs_limma 0.5712230
## basic_vs_noiseq 0.4952898
## deseq_vs_dream 0.5635112
## deseq_vs_ebseq 0.5971996
## deseq_vs_edger 0.8020767
## deseq_vs_limma 0.5464769
## deseq_vs_noiseq 0.5474146
## dream_vs_ebseq 0.6699376
## dream_vs_edger 0.7638931
## dream_vs_limma 0.9711045
## dream_vs_noiseq 0.7290749
## ebseq_vs_edger 0.7038600
## ebseq_vs_limma 0.6672232
## ebseq_vs_noiseq 0.7781966
## edger_vs_limma 0.7368432
## edger_vs_noiseq 0.7338731
## limma_vs_noiseq 0.7180644nasal_7sl_salmon_table <- combine_de_tables(nasal_7sl_salmon_de, excel = "excel/nasal_7sl_proxy_table.xlsx")## Deleting the file excel/nasal_7sl_proxy_table.xlsx before writing the tables.## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_vs_negative 20 159 27 35 31 12## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Plot describing unique/shared genes in a differential expression table.
nasal_7sl_hisat_table <- combine_de_tables(nasal_7sl_hisat_de, excel = "excel/nasal_7sl_hisat_proxy_table.xlsx")## Looking for subscript invalid names, end of extract_keepers.## A set of combined differential expression results.## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 positive_vs_negative 16 12 29 10 13 3## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
Oh, Maria Adelaida was actually looking only for the PBMC samples.
pbmc_nasal_7sl_se <- subset_se(hs_salmon, subset = "condition=='pbmcs'") %>%
set_conditions(fact = "nasal_7sl_status")## The numbers of samples by condition are:##
## negative positive
## 21 4## Setting colors with no pre-defined colors, using the Dark2 palette.pbmc_nasal_7sl_hisat_se <- subset_se(hs_hisat, subset = "condition=='pbmcs'") %>%
set_conditions(fact = "nasal_7sl_status")## The numbers of samples by condition are:##
## negative positive
## 25 5## Setting colors with no pre-defined colors, using the Dark2 palette.pbmc_nasal_hisat_norm <- normalize(pbmc_nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
norm = "quant", filter = TRUE)## Removing 9085 low-count genes (12486 remaining).## transform_counts: Found 29 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative, positive
## Shapes are defined by mRNA.
pbmc_nasal_hisat_nb <- normalize(pbmc_nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
batch = "svaseq", filter = "simple")## Removing 3837 low-count genes (17734 remaining).## transform_counts: Found 27306 values less than 0.## transform_counts: Found 27306 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative, positive
## Shapes are defined by mRNA.
pbmc_nasal_7sl_de <- all_pairwise(pbmc_nasal_7sl_se, filter = "simple", force = TRUE,
model_svs = "svaseq", model_fstring = "~ 0 + condition")## negative positive
## 21 4## Removing 4033 low-count genes (6852 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 93084 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative positive
## 21 4## calcNormFactors has been renamed to normLibSizes## conditions
## negative positive
## 21 4## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative positive
## 21 4## Deleting the file excel/pbmc_nasal_proxy.xlsx before writing the tables.## Looking for subscript invalid names, end of extract_keepers.17 TODO 202512
Repeat this nasal proxy test using each of the other cell types.
The factors of likely interest are: “wbcs” “nasal swab” ideally both “skin biopsy healthy” and “skin biopsy scar” but perhaps only “skin biopsy”.
wbc_nasal_7sl_se <- subset_se(hs_salmon, subset = "condition=='wbcs'") %>%
set_conditions(fact = "nasal_7sl_status")## The numbers of samples by condition are:##
## negative positive
## 19 4## Setting colors with no pre-defined colors, using the Dark2 palette.wbc_nasal_7sl_hisat_se <- subset_se(hs_hisat, subset = "condition=='wbcs'") %>%
set_conditions(fact = "nasal_7sl_status")## The numbers of samples by condition are:##
## negative positive
## 24 5## Setting colors with no pre-defined colors, using the Dark2 palette.wbc_nasal_hisat_norm <- normalize(wbc_nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
filter = "simple", norm = "quant")## Removing 4025 low-count genes (17546 remaining).## transform_counts: Found 71429 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative, positive
## Shapes are defined by mRNA.
wbc_nasal_hisat_nb <- normalize(wbc_nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
filter = "simple", batch = "svaseq")## Removing 4025 low-count genes (17546 remaining).## transform_counts: Found 27379 values less than 0.## transform_counts: Found 27379 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by negative, positive
## Shapes are defined by mRNA.
wbc_nasal_7sl_de <- all_pairwise(wbc_nasal_7sl_se, filter = "simple", force = TRUE,
model_svs = "svaseq", model_fstring = "~ 0 + condition")## negative positive
## 19 4## Removing 4252 low-count genes (6633 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 82619 entries to zero.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## This contrast put the denominator first.## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.
## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative positive
## 19 4## calcNormFactors has been renamed to normLibSizes## conditions
## negative positive
## 19 4## Warning in choose_binom_dataset(input, force = force): This data was inappropriately forced into integers.## conditions
## negative positive
## 19 4## Deleting the file excel/wbc_nasal_proxy.xlsx before writing the tables.## Looking for subscript invalid names, end of extract_keepers.18 Upload count tables for Mariana
## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
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## 'preprocessing/PRHU0070/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0071/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0072/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0073/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0074/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0075/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0076/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0077/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0078/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0079/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0080/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0081/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0082/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0083/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0084/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0085/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0086/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0087/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0088/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0089/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
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## 'preprocessing/PRHU0100/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0101/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0102/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning in utils::tar(output, files = include_list, compression = compression): storing paths of more than 100 bytes is not portable:
## 'preprocessing/PRHU0103/outputs/20250918hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz'## Warning: invalid uid value replaced by that for user 'nobody'## Warning: invalid uid value replaced by that for user 'nobody'19 Explicit comparison of library types
## The numbers of samples by condition are:##
## mRNA RZ
## 120 12## Setting colors with no pre-defined colors, using the Dark2 palette.## The number of samples by batch are:##
## nasalswab pbmcs skinbiopsynon-lesion skinbiopsyscar wbcs
## 25 30 27 21 29libtype_norm <- normalize(libtype_se, transform = "log2", convert = "cpm", norm = "quant", filter = TRUE)## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 21908 values equal to 0, adding 1 to the matrix.## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable convergence failure
## Warning in MASS::cov.trob(data[, vars], wt = weight * nrow(data)): Probable convergence failure
libtype_nb <- normalize(libtype_se, transform = "log2", convert = "cpm", batch = "svaseq", filter = TRUE)## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 24218 values less than 0.## transform_counts: Found 24218 values equal to 0, adding 1 to the matrix.
libtype_de <- all_pairwise(libtype_se, filter = TRUE, model_svs = "svaseq",
model_fstring = "~ 0 + condition")## mRNA RZ
## 120 12## Removing 5557 low-count genes (16014 remaining).## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## I think this is failing? SummarizedExperiment## Basic step 0/3: Transforming data.## Setting 408837 entries to zero.## This received a matrix of SVs.## converting counts to integer mode## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## This contrast put the denominator first.## conditions
## mRNA RZ
## 120 12## calcNormFactors has been renamed to normLibSizes## conditions
## mRNA RZ
## 120 12## conditions
## mRNA RZ
## 120 1220 202610: Clinical Presentation: Healthy vs not
As of 202610, we have 20 samples which were never infected and 113 which have visited the clinic and are currently deemed healthy. These span all the celltypes. Let us take a moment and look at them.
## The numbers of samples by condition are:##
## h-cl hd
## 113 19## Setting colors with no pre-defined colors, using the Dark2 palette.presentation_norm <- normalize(presentation_se, filter = TRUE, convert = "cpm",
norm = "quant", transform = "log2")## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 21908 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by h-cl, hd
## Shapes are defined by mRNA, RZ.
presentation_nb <- normalize(presentation_se, filter = TRUE, convert = "cpm",
batch = "svaseq", transform = "log2")## Removing 5557 low-count genes (16014 remaining).## transform_counts: Found 20200 values less than 0.## transform_counts: Found 20200 values equal to 0, adding 1 to the matrix.## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by h-cl, hd
## Shapes are defined by mRNA, RZ.
---
title: "Examining the human persistence samples"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: zenburn
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
---

<style type="text/css">
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
  font-size: 16px
}
body .main-container {
  max-width: 1600px;
}
</style>

```{r options, include=FALSE}
library(hpgltools)
library(ggplot2)
library(reticulate)
tt <- try(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(
  progress = TRUE, verbose = TRUE, width = 90, echo = TRUE)
knitr::opts_chunk$set(
  error = TRUE, fig.width = 8, fig.height = 8, fig.retina = 2,
  out.width = "100%", dev = "png",
  dev.args = list(png = list(type = "cairo-png")))
old_options <- options(stringsAsFactors = FALSE, knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
previous_file <- ""
ver <- format(Sys.Date(), "%Y%m%d")

##tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
rmd_file <- "human_samples_202610.Rmd"
```

# Introduction

I want to use this document to examine our first round of persistence
samples.  I checked my email from Najib and did not find a sample
sheet but did find an explanation of the three sample types we expect.

In preparation for this, I downloaded a new hg38 genome.  Since the
panamensis asembly has not significantly changed (excepting the
putative long read genome which I have not yet seen), I am just using
the same one.

# Loading annotation

The hg38 genome I got is from 202405.  The various ensembl archive servers have been having troubles
recently, so I may need to cherry-pick one which is working.  I will therefore also load annotations
from org.He.eg.db in case ensembl is not responding.

```{r}
## Ok, so useast.ensembl is failing today, let us use the jan2024 archive?
#hs_annot <- load_biomart_annotations(archive = FALSE, species = "hsapiens")
## Seems like the 202401 archive is a good choice, it is explicitly the hg38_111 release.
## and it is waaaaay faster (like 100x) than useast right now.

## Note, I am explicitly not using the archive in the following invocation because none of them were
## responding, so that was a whole big annoying thing.
hs_annot <- load_biomart_annotations(archive = FALSE, species = "hsapiens")
hs_orgdb <- load_orgdb_annotations("org.Hs.eg.db", fields = c("SYMBOL", "ENSEMBL", "ENTREZID"))[["genes"]]

panamensis_orgdb_idx <- grep(pattern = "^org.+panamen.+MHOM.+db$", x = rownames(installed.packages()))
panamensis_orgdb <- tail(rownames(installed.packages())[panamensis_orgdb_idx], n = 1)
lp_annot <- load_orgdb_annotations(panamensis_orgdb, keytype = "gid")

major_db <- "org.Lmajor.Friedlin.v68.eg.db"
library(major_db, character.only = TRUE)
all_columns <- keytypes(org.Lmajor.Friedlin.v68.eg.db)
wanted_idx <- grepl(pattern = "^ANNOT", x = all_columns)
wanted_columns <- all_columns[wanted_idx]
gene_ids <- keys(org.Lmajor.Friedlin.v68.eg.db)
major_annotations <- load_orgdb_annotations(major_db, keytype = "gid", fields = wanted_columns)
```

This is a little silly, but I am going to reload the annotations using
the previous invocation to extract the annotation table without having
to think.  The previous block loads the orgdb for me, so I can just
use that to get the fun annotations.

```{r}
lp_org_annot <- load_orgdb_annotations(panamensis_orgdb, keytype = "gid", fields = "^annot")
```

# Colors

I want a reminder to set the colors for the various states in the data.  A fun thing I did recently:
I can automagically pull colors from the backgrounds of cells in the sample sheet!  Not really
relevant right now, but fun.

```{r}
chosen_colors <- list(
  "sample_type" = list(),
  "detectionparasiteby7sl" = list(),
  "library_type" = list(),
  "nasal_7sl_status" = list(),
  "any_7sl_status" = list()
  "participant_code" = list(),
  "detection_type" = list(),
  "hs" = list(),
  "clinical_presentation" = list(),
  "sample_7sl" = list())
```

# Collect preprocessed metadata

Use my new sample sheet here.  I got the idea recently to make a copy of any freshly downloaded
sample sheet and give it the suffix '_readwrite' so that I remember to never open the samplesheet by
itself in rw.

```{r}
current_samplesheet <- "sample_sheets/Persistence_v2.1_20261007_readwrite.xlsx"
first_spec <- make_rnaseq_spec()
input <- read_metadata(current_samplesheet)
colnames(input)
summary(as.factor(input[["detectionparasiteby7sl"]]))

pre_meta <- gather_preprocessing_metadata(
  starting_metadata = current_samplesheet, id_column = "hpgl_identifier",
  specification = first_spec, new_metadata = "persistence_hs_modified.xlsx",
  basedir = "preprocessing", species = c("hg38_115", "lpanamensis_mhomcol_v68"))
summary(as.factor(pre_meta[["new_meta"]][["detectionparasiteby7sl"]]))
## I think I will force everything to lowercase no spaces
modified_meta <- pre_meta[["new_meta"]]

## Added the following line to gather_preprocessing_metadata()
rownames(modified_meta) <- make.names(modified_meta[["hpgl_identifier"]], unique = TRUE)

## FIXME: 202511: I broke something in some of these functions and it is pulling
## the wrong information for number of observed genes.
head(modified_meta)
head(modified_meta[["salmon_observed_genes_hg38_115"]])
modified_meta[["detectionparasiteby7sl"]] <- sanitize_metadata(
  modified_meta[["detectionparasiteby7sl"]], lower = TRUE, spaces = TRUE, punct = FALSE)
summary(modified_meta[["detectionparasiteby7sl"]])
```

I would like to have a combination of clinical presentation and sampletype as well:

```{r}
modified_meta[["sample_type"]] <- sanitize_metadata(
  modified_meta[["sample_type"]], lower = TRUE, spaces = TRUE, punct = FALSE)
summary(modified_meta[["sample_type"]])
modified_meta[["clinical_presentation"]] <- sanitize_metadata(
  modified_meta[["clinical_presentation"]], lower = TRUE, spaces = TRUE, punct = FALSE)
summary(modified_meta[["clinical_presentation"]])
modified_meta[["type_presentation"]] <- as.factor(paste0(as.character(modified_meta[["sample_type"]]), "_",
                                                         as.character(modified_meta[["clinical_presentation"]])))
summary(modified_meta[["type_presentation"]])
```

Create a factor of the 7SL detection of nasal samples.  Note, we need
to recast the NAs as undefined.  I would have sworn that my gather
function would do that?

```{r}
modified_meta[["nasal_7sl_status"]] <- as.character(modified_meta[["detectionparasiteby7sl"]])

nasal_samples <- modified_meta[["sample_type"]] == "nasalswab"
summary(nasal_samples)
sl_positive <- modified_meta[["nasal_7sl_status"]] == "positive"
summary(sl_positive)
nasal_positive <- nasal_samples & sl_positive
summary(nasal_positive)
nasal_positive_samples <- rownames(modified_meta)[nasal_positive]
nasal_positive_people <- modified_meta[nasal_positive_samples, "participant_code"]
nasal_positive_people
nasal_positive_people_samples <- modified_meta[["participant_code"]] %in% nasal_positive_people
modified_meta[["nasal_7sl_status"]] <- "negative"
modified_meta[nasal_positive_people_samples, "nasal_7sl_status"] <- "positive"
modified_meta[["nasal_7sl_status"]] <- as.factor(modified_meta[["nasal_7sl_status"]])
summary(modified_meta[["nasal_7sl_status"]])
```

Similarly create a factor of people with any 7SL positive sample.

```{r}
modified_meta[["any_7sl_status"]] <- "negative"
positive_samples <- as.character(modified_meta[["detectionparasiteby7sl"]]) == "positive"
positive_people <- modified_meta[positive_samples, "participant_code"]
positive_people_idx <- !duplicated(positive_people)
positive_people_sorted <- sort(positive_people[positive_people_idx])
persister_people_samples <- modified_meta[["participant_code"]] %in% positive_people_sorted
modified_meta[persister_people_samples, "any_7sl_status"] <- "positive"
modified_meta[["any_7sl_status"]] <- as.factor(modified_meta[["any_7sl_status"]])
summary(modified_meta[["any_7sl_status"]])
positive_people_sorted
```

## Add combination of detection and library type

Also add a category separating all skin samples from everything else.

```{r}
modified_meta[["detection_type"]] <- as.factor(
  paste0(as.character(modified_meta[["detectionparasiteby7sl"]]), "_",
         as.character(modified_meta[["library_type"]])))
summary(modified_meta[["detection_type"]])

modified_meta[["skinp"]] <- "not_skin"
skin_idx <- grepl(x = modified_meta[["sample_type"]], pattern = "^skin")
summary(skin_idx)
modified_meta[skin_idx, "skinp"] <- "skin"
modified_meta[["skinp"]] <- as.factor(modified_meta[["skinp"]])
summary(modified_meta[["skinp"]])
```

# The purpose of the post 202608 samples

In the last round of sequencing of human samples, we received a series of purely healthy samples;
these are from individuals with no history of Leishmaniasis and are intended to provide a comparison
point against everything else we are looking at in this dataset.

```{r}
human_meta_idx <- modified_meta[["aim"]] == "aim1"
human_meta <- modified_meta[human_meta_idx, ]
table(human_meta[["clinical_presentation"]])
table(human_meta[["type_presentation"]])
```

Thus, in theory we can perform a series of comparisons among the 'Clinical Presentation' factor in
the sample design.  By itself, this would give us a 20 vs. 113 sample comparison of healthy people
vs. people who came to the clinic.

# Collect gene annotations

I should have all my load_xyz_annotation functions return some of the
same elements in their retlists.

```{r}
lp_genes <- lp_annot[["genes"]]
hg_genes <- hs_annot[["gene_annotations"]]
```

# Quick peek at the SL samples, hg38 release 115

## Gather the transcript and gene annotations.

```{r}
hg_tx <- hs_annot[["annotation"]]
hg_map <- hs_annot[["gene_tx_map"]]
lp_genes <- lp_annot[["genes"]]
```

## Figure out which samples do not have 7SL categories

```{r}
undef_7sl <- is.na(human_meta[["detectionparasiteby7sl"]])
summary(undef_7sl)
sample_7sl <- paste0(human_meta[["sample_type"]], "_",
                     human_meta[["detectionparasiteby7sl"]])
sample_7sl <- as.factor(gsub(x = sample_7sl, pattern = "[[:space:]]", replacement = "_"))
summary(sample_7sl)
human_meta[["sample_7sl"]] <- sample_7sl
```

## Healthy vs scar samples

```{r}
healthy_vs_scar <- gsub(x = human_meta[["sample_type"]],
                        pattern = "^skinbiopsy ", replacement = "")
human_meta[["hs"]] <- NA
skin_samples <- grepl(x = human_meta[["sample_type"]], pattern = "^skin")
human_meta[skin_samples, "hs"] <- "healthy"
scar_samples <- grepl(x = human_meta[["sample_type"]], pattern = "scar")
human_meta[scar_samples, "hs"] <- "scar"
human_meta[["hs"]] <- as.factor(human_meta[["hs"]])
summary(human_meta[["hs"]])
```

## Clean up missing 7SL detection samples

I think the last of these has been fixed since the last time I updated
this sheet.

```{r}
undef_7sl <- is.na(colData(hs_hisat)[["detectionparasiteby7sl"]])
summary(undef_7sl)
if (sum(undef_7sl)) {
  colData(hs_salmon)[undef_7sl, "detectionparasiteby7sl"] <- "unknown"
  colData(hs_hisat)[undef_7sl, "detectionparasiteby7sl"] <- "unknown"
} else {
  message("There appear to be no missing 7SL entries.")
}
```

## Print current state

```{r}
table(human_meta[["library_type"]])
table(human_meta[["detection_type"]])
table(human_meta[["detectionparasiteby7sl"]])
table(human_meta[["clinical_presentation"]])
```

## Combine 7SL status and the sample type into one factor

```{r}
sample_7sl <- as.factor(paste0(human_meta[["sample_type"]], "_",
                               human_meta[["detectionparasiteby7sl"]]))
summary(sample_7sl)
```

## Create initial hisat/salmon tables

```{r}
hs_salmon <- create_se(human_meta, gene_info = hg_tx,
                       tx_gene_map = hg_map, file_column = "salmon_count_table_hg38_115") %>%
  set_conditions(fact = "sample_type") %>%
  set_batches(fact = "library_type")

hs_hisat <- create_se(human_meta, gene_info = hg_genes,
                      file_column = "hisat_count_table_hg38_115") %>%
  set_conditions(fact = "sample_type") %>%
  set_batches(fact = "library_type")
```


### Write out cpm values of all samples

```{r}
hs_salmon_cpm <- write_normalized_se(
  hs_salmon, excel = glue("excel/cpm/hs_salmon_all_samples-v{ver}.xlsx"))
hs_hisat_cpm <- write_normalized_se(
  hs_hisat, excel = glue("excel/cpm/hs_hisat_all_samples-v{ver}.xlsx"))
```

## Separate the mRNA samples from ribo-zero

Note, when we are finished, we will be using only the mRNA samples and
ignoring the ribo-zero.  But there are some questions about the data
provided by the two libraries.

```{r}
hs_salmon_mrna <- set_conditions(hs_salmon, fact = "sample_type") %>%
  subset_se(subset = "library_type=='mRNA'") %>%
  set_batches("detectionparasiteby7sl")

hs_salmon_rz <- set_conditions(hs_salmon, fact = "sample_type") %>%
  subset_se(subset = "library_type=='RZ'") %>%
  set_batches("detectionparasiteby7sl")

hs_hisat_mrna <- set_conditions(hs_hisat, fact = "sample_type") %>%
  subset_se(subset = "library_type=='mRNA'") %>%
  set_batches("detectionparasiteby7sl")

hs_hisat_rz <- set_conditions(hs_hisat, fact = "sample_type") %>%
  subset_se(subset = "library_type=='RZ'") %>%
  set_batches("detectionparasiteby7sl")
```

### Write those out as well

```{r}
hs_salmon_mrna_cpm <- write_normalized_se(
  hs_salmon_mrna, excel = glue("excel/cpm/hs_salmon_mrna_samples-v{ver}.xlsx"))
hs_salmon_rz_cpm <- write_normalized_se(
  hs_salmon_rz, excel = glue("excel/cpm/hs_salmon_rz_samples-v{ver}.xlsx"))

hs_hisat_mrna_cpm <- write_normalized_se(
  hs_hisat_mrna, excel = glue("excel/cpm/hs_hisat_mrna_samples-v{ver}.xlsx"))
hs_hisat_rz_cpm <- write_normalized_se(
  hs_hisat_rz, excel = glue("excel/cpm/hs_hisat_rz_samples-v{ver}.xlsx"))
```

## Extract only the healthy/scar samples, only mRNA

```{r}
hs_salmon_mrna_health <- subset_se(hs_salmon, subset = "hs=='healthy'|hs=='scar'") %>%
  set_conditions(fact = "hs")

hs_hisat_mrna_health <- subset_se(hs_hisat_mrna, subset = "hs=='healthy'|hs=='scar'") %>%
  set_conditions(fact = "hs")
```

### Write these out

```{r}
hs_salmon_mrna_health_cpm <- write_normalized_se(
  hs_salmon_mrna_health, excel = glue("excel/cpm/hs_salmon_mrna_health_samples-v{ver}.xlsx"))
hs_hisat_mrna_health_cpm <- write_normalized_se(
  hs_hisat_mrna_health, excel = glue("excel/cpm/hs_hisat_mrna_health_samples-v{ver}.xlsx"))
```

# HU Metadata

## Percent of reads mapped to the genome by hisat

```{r}
hs_mapped_mrna <- plot_metadata_factor(hs_hisat_mrna, column = "hisat_genome_percent_log_hg38_115")
pp(file = "images/hs_mapped_mrna_violin.png", image = hs_mapped_mrna)


hs_mapped_rz <- plot_metadata_factor(hs_hisat_rz, column = "hisat_genome_percent_log_hg38_115")
pp(file = "images/hs_mapped_rz_violin.png", image = hs_mapped_rz)
```

## Number of gene observed by salmon

```{r}
hs_observed_mrna <- plot_metadata_factor(hs_hisat_mrna, column = "salmon_observed_genes_hg38_115")
hs_observed_mrna

hs_observed_rz <- plot_metadata_factor(hs_hisat_rz, column = "salmon_observed_genes_hg38_115")
hs_observed_rz
```

## Percent reads quantified to the human transcriptome by salmon

```{r}
hs_pct_mrna <- plot_metadata_factor(hs_salmon_mrna, column = "salmon_percent_hg38_115")
hs_pct_mrna

hs_pct_rz <- plot_metadata_factor(hs_salmon_rz, column = "salmon_percent_hg38_115")
hs_pct_rz
```

## Number of kraken identified bacterial reads

While at this, add a column dividing the kraken bacterial classified
reads from the total reads.

```{r}
hs_kraken_mrna <- plot_metadata_factor(hs_salmon_mrna, column = "kraken_bacterial_classified")
pp(file = "images/hs_kraken_bacterial_reads_mrna_libraries.png", image = hs_kraken_mrna)

hs_kraken_rz <- plot_metadata_factor(hs_salmon_rz, column = "kraken_bacterial_classified")
pp(file = "images/hs_kraken_bacterial_reads_rz_libraries.png", image = hs_kraken_rz)

colData(hs_salmon_mrna)[["pct_kraken_bacterial"]] <- colData(hs_salmon_mrna)[["kraken_bacterial_classified"]] /
  colData(hs_salmon_mrna)[["trimomatic_output"]]
colData(hs_salmon_rz)[["pct_kraken_bacterial"]] <- colData(hs_salmon_rz)[["kraken_bacterial_classified"]] /
  colData(hs_salmon_rz)[["trimomatic_output"]]
hs_kraken_pct_mrna <- plot_metadata_factor(hs_salmon_mrna, column = "pct_kraken_bacterial")
pp(file = "images/hs_kraken_bacterial_pct_reads_mrna_libraries.png", image = hs_kraken_pct_mrna)

hs_kraken_pct_rz <- plot_metadata_factor(hs_salmon_rz, column = "pct_kraken_bacterial")
pp(file = "images/hs_kraken_bacterial_pct_reads_rz_libraries.png", image = hs_kraken_pct_rz)
```

## Sankey of a few factors

```{r}
hs_sankey <- plot_meta_sankey(
  hs_salmon, factors = c("detectionparasiteby7sl", "sample_type", "library_type"))
pp(file = "images/hs_detect_sampletype_libtype_sankey.png", image = hs_sankey[["plot"]])

hs_sankey <- plot_meta_sankey(
  hs_salmon, factors = c("sample_type", "detectionparasiteby7sl", "library_type"))
pp(file = "images/hs_sampletype_detection_libtype_sankey.png", image = hs_sankey[["plot"]])
```

# Write out the metadata in its current state

```{r}
write_xlsx(data = modified_meta, excel = "sample_sheets/human_samples_202511_with_nasal_factor.xlsx")
```

# nonzero/libsize/etc

```{r}
plot_legend(hs_salmon)
pp(file = "images/hs_salmon_quantreads.png", image = plot_libsize(hs_salmon, yscale = FALSE),
   width = 24, height = 9)
pp(file = "images/hs_salmon_nonzero.png", image = plot_nonzero(hs_salmon, y_intercept = 0.40))

pp(file = "images/hs_hisat_quantreads.png", image = plot_libsize(hs_hisat, yscale = FALSE),
   width = 24, height = 9)
pp(file = "images/hs_hisat_nonzero.png", image = plot_nonzero(hs_hisat))

pp(file = "images/hs_hisat_mrna_libsize.png", image = plot_libsize(hs_hisat_mrna))
pp(file = "images/hs_hisat_rz_libsize.png", image = plot_libsize(hs_hisat_rz))
```

# Extract the nasal samples and see how many mRNA and ribozero are of the same person

```{r}
hs_hisat_nasal <- subset_se(hs_hisat, subset = "sample_type=='nasalswab'") %>%
  set_conditions(fact = "detectionparasiteby7sl")
person_libtype <- colData(hs_hisat_nasal)[, c("participant_code", "library_type")]
table(person_libtype)
## We have both library types for 4 people: 1009, 1011, 2001, and 2020.

person_libtype_model_nbb <- set_batches(hs_hisat_nasal, fact = "library_type") %>%
  normalize(transform = "log2", convert = "cpm", batch = "pca", filter = TRUE) %>%
  normalize(batch = "svaseq")
newnames <- colData(hs_hisat_nasal)[["participant_code"]]
pp(file = "images/pca_batch_then_sva.png",
   image = plot_pca(person_libtype_model_nbb, exp_names = newnames, plot_labels = TRUE),
   width = 12, height = 12)

person_presentation <- colData(hs_hisat_nasal)[, c("participant_code", "clinical_presentation")]
table(person_presentation)
## Note to self, person 2001 is our lone nasal healthy donor.

hs_hisat_nasal_nohealthy <- subset_se(hs_hisat_nasal, subset = "clinical_presentation!='HD'")
hs_hisat_nasal_nohealthy
newnames <- colData(hs_hisat_nasal_nohealthy)[["participant_code"]]
hs_hisat_nasal_nh_norm <- normalize(hs_hisat_nasal_nohealthy, transform = "log2", convert = "cpm",
                                    filter = TRUE, norm = "quant")
pp(file = "images/hs_hisat_nasal_nh_norm_pca.png",
   image = plot_pca(hs_hisat_nasal_nh_norm, exp_names = newnames, plot_labels = TRUE))

hs_hisat_nasal_nh_nb <- normalize(hs_hisat_nasal_nohealthy, transform = "log2", convert = "cpm",
                                  filter = TRUE, batch = "svaseq")
pp(file = "images/hs_hisat_nasal_nh_nb_pca.png",
   image = plot_pca(hs_hisat_nasal_nh_nb, exp_names = newnames, plot_labels = TRUE))

hs_hisat_nasal_nh_norm <- set_batches(hs_hisat_nasal_nh_norm, fact = "participant_code")
plot_pca(hs_hisat_nasal_nh_norm)

hs_hisat_nasal_nh_de <- all_pairwise(hs_hisat_nasal_nohealthy, filter = TRUE, model_svs = "svaseq",
                                     model_fstring = "~ 0 + condition")
hs_hisat_nasal_nh_table <- combine_de_tables(
  hs_hisat_nasal_nh_de, excel = "excel/hs_hisat_nasal_nh_table.xlsx")
```

Try removing the samples for which we have a duplicate/person across
mRNA and Rz, removing only the Rz samples.

The cheesy easy way to do this is to just check for person PP1009,
1011, 2001 2020 && library_type = RZ, but it should be trivial to do
it in a smarter way... But I will probably mess that up the first time
and don't want to be too annoying.

## We have both library types for 4 people: 1009, 1011, 2001, and 2020.

```{r}
excluded_duplicates <- subset_se(hs_hisat_nasal_nohealthy,
                                 subset = "!(participant_code=='PP1009'&library_type=='RZ')") %>%
  subset_se(subset = "!(participant_code=='PP1011'&library_type=='RZ')") %>%
  subset_se(subset = "!(participant_code=='PP2001'&library_type=='RZ')") %>%
  subset_se(subset = "!(participant_code=='PP2020'&library_type=='RZ')")

excluded_duplicates_norm <- normalize(excluded_duplicates, transform = "log2", convert = "cpm",
                                      norm = "quant", filter = TRUE)
newnames <- colData(excluded_duplicates)[["participant_code"]]
pp(file = "images/excluded_duplicates_norm_pca.png",
   image = plot_pca(excluded_duplicates_norm, exp_names = newnames, plot_labels = TRUE))

excluded_duplicates_nb <- normalize(excluded_duplicates, transform = "log2", convert = "cpm",
                                    batch = "svaseq", filter = TRUE)
pp(file = "images/excluded_duplicates_nb_pca.png",
   image = plot_pca(excluded_duplicates_nb, exp_names = newnames, plot_labels = TRUE))

excluded_duplicates_de <- all_pairwise(excluded_duplicates, filter = TRUE, model_svs = "svaseq",
                                       model_fstring = "~ 0 + condition")
excluded_duplicates_table <- combine_de_tables(excluded_duplicates_de, excel = "excel/excluded_duplicates_table.xlsx")
excluded_duplicates_table
excluded_duplicates_sig <- extract_significant_genes(excluded_duplicates_table, excel = "excel/excluded_duplicates_sig.xlsx")
excluded_duplicates_sig

excluded_up <- excluded_duplicates_sig[["deseq"]][["ups"]][["positive_vs_negative"]]
excluded_down <- excluded_duplicates_sig[["deseq"]][["downs"]][["positive_vs_negative"]]

excluded_up_gp <- simple_gprofiler(excluded_up)
excluded_up_gp
excluded_up_gp_bp_plots <- plot_enrichresult(excluded_up_gp[["BP_enrich"]])
pp(file = "images/excluded_up_gp_bp_go.png", image = excluded_up_gp_bp_plots[["go"]])
pp(file = "images/excluded_up_gp_bp_dot.png", image = excluded_up_gp_bp_plots[["dot"]])

excluded_down_gp <- simple_gprofiler(excluded_down)
excluded_down_gp
excluded_down_gp_bp_plots <- plot_enrichresult(excluded_down_gp[["BP_enrich"]])
pp(file = "images/excluded_down_gp_bp_go.png", image = excluded_down_gp_bp_plots[["go"]])
pp(file = "images/excluded_down_gp_bp_dot.png", image = excluded_down_gp_bp_plots[["dot"]])
```

# Normalize

A couple plots of all the salmon samples colored by sample type.

```{r}
hs_salmon_norm <- normalize(hs_salmon, transform = "log2", convert = "cpm",
                            filter = TRUE, norm = "quant")
hs_salmon_corheat <- plot_corheat(hs_salmon_norm)
pp(file = "images/hs_salmon_sampletype_pca.png", image = plot_pca(hs_salmon_norm))

hs_hisat_norm <- normalize(hs_hisat, transform = "log2", convert = "cpm",
                           filter = TRUE, norm = "quant")
hs_hisat_corheat <- plot_corheat(hs_hisat_norm)
pp(file = "images/hs_hisat_sampletype_corheat.png")
hs_hisat_corheat
dev.off()
hs_hisat_corheat
hs_hisat_pca <- plot_pca(hs_hisat_norm)
pp(file = "images/hs_hisat_sampletype_pca.png", image = plot_pca(hs_hisat_norm))
```

## Remove samples without 7SL detection state

While we are at it, color by the 7SL detection and shape by sample
type (Rz/mRNA).

In this instance I have separate colors for 7SL+ and 7SL- combined
with celltype.

```{r}
hs_hisat_detected <- subset_se(hs_hisat, subset = "detectionparasiteby7sl!='unknown'") %>%
  subset_se(subset = "detectionparasiteby7sl!='notapplicable'") %>%
  set_conditions(fact = "sample_7sl") %>%
  set_batches("sample_type")
```

Print a PCA of this using our generic normalization.

```{r}
hs_hisat_detect_norm <- normalize(hs_hisat_detected, transform = "log2", convert = "cpm",
                                  filter = TRUE, norm = "quant")
hs_hisat_detect_pca <- plot_pca(hs_hisat_detect_norm)
pp(file = "images/hs_hisat_7sldetected_pca.png", image = hs_hisat_detect_pca, width = 18, height = 9)
```

Followed by the same samples treated with SVA.

```{r}
hs_hisat_detect_nb <- normalize(hs_hisat_detected, transform = "log2", convert = "cpm",
                                filter = TRUE, batch = "svaseq")
pp(file = "images/hs_hisat_7sldetected_sva_pca.png",
   image = plot_pca(hs_hisat_detect_nb), height = 9, width = 18)

hs_hisat_only_7sl <- set_conditions(hs_hisat_detected, fact = "detectionparasiteby7sl")
hs_hisat_7sl_nb <- normalize(hs_hisat_only_7sl, transform = "log2", convert = "cpm",
                             batch = "svaseq", filter = TRUE)
pp(file = "images/hs_hisat_7sl_sva_pca.png", image = plot_pca(hs_hisat_7sl_nb),
   height = 9, width = 18)
```

# Compare distribution of RZ/Stranded libraries

Maria Adelaida is interested in the distribution of the relatively few
rz samples vs the relatively large number of stranded mRNA libraries.

I think it is likely that the nasal samples are of primary interest.

Oh, it turns out the automatically generated colors for this are
terrible.  Maybe we do not care, there are so few 7SL positive mRNA
samples that it is basically irrelevant.

```{r}
hs_hisat_mrna_7sl <- set_conditions(hs_hisat_mrna, fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "sample_type")
hs_hisat_mrna_7sl_norm <- normalize(hs_hisat_mrna_7sl, convert = "cpm", filter = TRUE,
                                    norm = "quant", transform = "log2")
## This still clusters primarily by sample type, and there are precious few positive samples.
plot_pca(hs_hisat_mrna_7sl_norm)
```

Replot this following treatment with SVA.  At least in this instance
it is possible to differentiate the sample types in the blob.

```{r}
hs_hisat_mrna_7sl_nb <- normalize(hs_hisat_mrna_7sl, convert = "cpm", filter = TRUE,
                                  batch = "sva", transform = "log2")
hs_hisat_mrna_sva_pca <- plot_pca(hs_hisat_mrna_7sl_nb)
pp(file = "images/hs_hisat_mrna_sva_pca.png")
hs_hisat_mrna_sva_pca
dev.off()
hs_hisat_mrna_sva_pca
```

## Perform DESeq using these

```{r}
table(conditions(hs_hisat_mrna_7sl))
hs_hisat_mrna_7sl_de <- all_pairwise(hs_hisat_mrna_7sl, model_fstring = "~ 0 + condition",
                                     model_svs = "svaseq", filter = TRUE, force = TRUE)
hs_hisat_mrna_7sl_de
pp(file = "images/7sl_only_de_corheat.png", image = hs_hisat_mrna_7sl_de[[""]])

hs_hisat_mrna_7sl_table <- combine_de_tables(
  hs_hisat_mrna_7sl_de,
  excel = glue("excel/de_tables/hs_hisat_all_mrna_7sl_table-v{ver}.xlsx"))
hs_hisat_mrna_7sl_table
pp(file = "images/sig_deseq_7sl_genes.png", image = hs_hisat_mrna_7sl_table[["plots"]][["positive_mRNA_vs_negative_mRNA"]][["deseq_vol_plots"]])

hs_hisat_mrna_7sl_sig <- extract_significant_genes(
  hs_hisat_mrna_7sl_table,
  excel = glue("excel/de_sig/hs_hisat_all_mrna_7sl_sig-v{ver}.xlsx"))
hs_hisat_mrna_7sl_sig
```

### Salmon nasal samples, mRNA libraries

Now restrict to just the nasal samples.

```{r}
hs_salmon_nasal_mrna_7sl <- subset_se(hs_salmon_mrna, subset = "sample_type=='nasalswab'") %>%
  set_conditions(fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "participant_code")

hs_hisat_nasal_mrna_7sl <- subset_se(hs_hisat_mrna, subset = "sample_type=='nasalswab'") %>%
  set_conditions(fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "participant_code")
```

Print the normalized PCA showing only a couple of positive samples in
this set.

Small note: I slightly changed how set_conditions works to drop unused levels.

```{r}
hs_hisat_nasal_mrna_7sl_norm <- normalize(hs_hisat_nasal_mrna_7sl, convert = "cpm",
                                          filter = TRUE, norm = "quant", transform = "log2")
hs_hisat_nasal_mrna_7sl_pca <- plot_pca(hs_hisat_nasal_mrna_7sl_norm)
pp(file = "images/hs_hisat_nasal_mrna_7sl_norm_pca.png",
   image = hs_hisat_nasal_mrna_7sl_pca)
```

```{r}
hs_hisat_nasal_mrna_7sl_nb <- normalize(hs_hisat_nasal_mrna_7sl, convert = "cpm", filter = TRUE,
                                        batch = "sva", transform = "log2")
hs_hisat_nasal_mrna_7sl_nb_pca <- plot_pca(hs_hisat_nasal_mrna_7sl_nb)
pp(file = "images/hs_hisat_nasal_mrna_7sl_sva_pca.png",
   image = hs_hisat_nasal_mrna_7sl_nb_pca)
```

### Perform pairwise analysis of these

```{r}
hs_hisat_nasal_mrna_7sl_de <- all_pairwise(hs_hisat_nasal_mrna_7sl, model_fstring = "~ 0 + condition",
                                           model_svs = "svaseq", filter = TRUE, force = TRUE)
hs_hisat_nasal_mrna_7sl_de

hs_hisat_nasal_mrna_7sl_table <- combine_de_tables(
  hs_hisat_nasal_mrna_7sl_de,
  excel = glue("excel/de_tables/hs_hisat_nasal_mrna_7sl_table-v{ver}.xlsx"))
hs_hisat_nasal_mrna_7sl_table

hs_hisat_nasal_mrna_7sl_sig <- extract_significant_genes(
  hs_hisat_nasal_mrna_7sl_table,
  excel = glue("excel/de_sig/hs_hisat_nasal_mrna_7sl_sig-v{ver}.xlsx"))
hs_hisat_nasal_mrna_7sl_sig
```

## Repeat with the ribo zero samples

### 7SL samples, ribozero

Extract the ribozero samples into a new datastructure.

```{r}
hs_hisat_rz_7sl <- set_conditions(hs_hisat_rz, fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "sample_type")

hs_salmon_rz_7sl <- set_conditions(hs_salmon_rz, fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "sample_type")
```

and play with them.

```{r}
hs_hisat_rz_7sl_norm <- normalize(hs_hisat_rz_7sl, convert = "cpm", filter = TRUE,
                                  norm = "quant", transform = "log2")
## This still clusters primarily by sample type, and there are precious few positive samples.
hs_hisat_rz_7sl_norm_pca <- plot_pca(hs_hisat_rz_7sl_norm)
pp(file = "imaegs/hs_hisat_rz_7sl_norm_pca.png",
   image = hs_hisat_rz_7sl_norm_pca)

hs_salmon_rz_7sl_norm <- normalize(hs_salmon_rz_7sl, convert = "cpm", filter = TRUE,
                                   norm = "quant", transform = "log2")
plot_pca(hs_salmon_rz_7sl_norm)
```

```{r}
hs_hisat_rz_7sl_de <- all_pairwise(hs_hisat_rz_7sl, model_fstring = "~ 0 + condition",
                                   model_svs = "svaseq", filter = TRUE, force = TRUE)
hs_hisat_rz_7sl_de
hs_hisat_rz_7sl_table <- combine_de_tables(
  hs_hisat_rz_7sl_de,
  excel = glue("excel/de_tables/hs_hisat_rz_7sl_table-v{ver}.xlsx"))
hs_hisat_rz_7sl_table
hs_hisat_rz_7sl_sig <- extract_significant_genes(
  hs_hisat_rz_7sl_table,
  excel = glue("excel/de_sig/hs_hisat_rz_7sl_sig-v{ver}.xlsx"))
hs_hisat_rz_7sl_sig
```

### Salmon 7SL samples, ribozero

```{r}
hs_salmon_rz_7sl_de <- all_pairwise(hs_salmon_rz_7sl, model_fstring = "~ 0 + condition",
                                   model_svs = "svaseq", filter = TRUE, force = TRUE)
hs_salmon_rz_7sl_de
hs_salmon_rz_7sl_table <- combine_de_tables(
  hs_salmon_rz_7sl_de,
  excel = glue("excel/de_tables/hs_salmon_rz_7sl_table-v{ver}.xlsx"))
hs_salmon_rz_7sl_table
hs_salmon_rz_7sl_sig <- extract_significant_genes(
  hs_salmon_rz_7sl_table,
  excel = glue("excel/de_sig/hs_salmon_rz_7sl_sig-v{ver}.xlsx"))
hs_salmon_rz_7sl_sig
```

### 7SL nasal samples, ribozero

Now restrict to just the nasal samples.

```{r}
hs_hisat_nasal_rz_7sl <- subset_se(hs_hisat_rz_7sl, subset = "sample_type=='nasalswab'") %>%
  set_conditions(fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "sample_type")

hs_hisat_nasal_rz_7sl_norm <- normalize(hs_hisat_nasal_rz_7sl, convert = "cpm", filter = TRUE,
                                        norm = "quant", transform = "log2")
hs_hisat_nasal_rz_7sl_npca <- plot_pca(hs_hisat_nasal_rz_7sl_norm)
pp(file = "images/hs_hisat_nasal_rz_7sl_npca.png")
hs_hisat_nasal_rz_7sl_npca[["plot"]]
dev.off()
hs_hisat_nasal_rz_7sl_npca

hs_hisat_nasal_rz_7sl_nb <- normalize(hs_hisat_nasal_rz_7sl, convert = "cpm", filter = TRUE,
                                    batch = "sva", transform = "log2")
hs_hisat_nasal_rz_7sl_nbpca <- plot_pca(hs_hisat_nasal_rz_7sl_nb)
pp(file = "images/hs_hisat_nasal_rz_7sl_nbpca.png")
hs_hisat_nasal_rz_7sl_nbpca[["plot"]]
dev.off()
hs_hisat_nasal_rz_7sl_nbpca
```

```{r}
hs_salmon_nasal_rz_7sl <- subset_se(hs_salmon_rz_7sl, subset = "sample_type=='nasalswab'") %>%
  set_conditions(fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "sample_type")

hs_salmon_nasal_rz_7sl_norm <- normalize(hs_salmon_nasal_rz_7sl, convert = "cpm", filter = TRUE,
                                        norm = "quant", transform = "log2")
hs_salmon_nasal_rz_7sl_npca <- plot_pca(hs_salmon_nasal_rz_7sl_norm)
pp(file = "images/hs_salmon_nasal_rz_7sl_npca.png")
hs_salmon_nasal_rz_7sl_npca[["plot"]]
dev.off()
hs_salmon_nasal_rz_7sl_npca

hs_salmon_nasal_rz_7sl_nb <- normalize(hs_salmon_nasal_rz_7sl, convert = "cpm", filter = TRUE,
                                    batch = "sva", transform = "log2")
hs_salmon_nasal_rz_7sl_nbpca <- plot_pca(hs_salmon_nasal_rz_7sl_nb)
pp(file = "images/hs_salmon_nasal_rz_7sl_nbpca.png")
hs_salmon_nasal_rz_7sl_nbpca[["plot"]]
dev.off()
hs_salmon_nasal_rz_7sl_nbpca
```

#### DE of this ever shrinking set

```{r}
hs_hisat_nasal_rz_7sl_de <- all_pairwise(hs_hisat_nasal_rz_7sl, model_fstring = "~ 0 + condition",
                                         model_svs = "svaseq", filter = TRUE, force = TRUE)
hs_hisat_nasal_rz_7sl_de
hs_hisat_nasal_rz_7sl_table <- combine_de_tables(
  hs_hisat_nasal_rz_7sl_de,
  excel = glue("excel/de_tables/hs_hisat_nasal_rz_7sl_table-v{ver}.xlsx"))
hs_hisat_nasal_rz_7sl_table
hs_hisat_nasal_rz_7sl_sig <- extract_significant_genes(
  hs_hisat_nasal_rz_7sl_table,
  excel = glue("excel/de_sig/hs_hisat_nasal_rz_7sl_sig-v{ver}.xlsx"))
hs_hisat_nasal_rz_7sl_sig
```

```{r}
hs_salmon_nasal_rz_7sl_de <- all_pairwise(hs_salmon_nasal_rz_7sl, model_fstring = "~ 0 + condition",
                                         model_svs = "svaseq", filter = TRUE, force = TRUE)
hs_salmon_nasal_rz_7sl_de
hs_salmon_nasal_rz_7sl_table <- combine_de_tables(
  hs_salmon_nasal_rz_7sl_de,
  excel = glue("excel/de_tables/hs_salmon_nasal_rz_7sl_table-v{ver}.xlsx"))
hs_salmon_nasal_rz_7sl_table
hs_salmon_nasal_rz_7sl_sig <- extract_significant_genes(
  hs_salmon_nasal_rz_7sl_table,
  excel = glue("excel/de_sig/hs_salmon_nasal_rz_7sl_sig-v{ver}.xlsx"))
hs_salmon_nasal_rz_7sl_sig
```

## Compare 7SL status for the PBMC samples

```{r}
hs_hisat_pbmc_7sl <- subset_se(hs_hisat, subset = "sample_type=='pbmcs'") %>%
  set_conditions(fact = "detectionparasiteby7sl")

hs_hisat_pbmc_mrna_7sl_norm <- normalize(hs_hisat_pbmc_7sl, transform = "log2",
                                        convert = "cpm", norm = "quant",
                                        filter = TRUE)
hs_hisat_pbmc_mrna_7sl_npca <- plot_pca(hs_hisat_pbmc_mrna_7sl_norm)
hs_hisat_pbmc_mrna_7sl_npca
pp(file = "images/hs_hisat_pbmc_mrna_7sl_npca.png",
   image = hs_hisat_pbmc_mrna_7sl_npca$plot)
```

## Extract the blood cells (not PBMC)

### Hisat

```{r}
hs_hisat_wbc_mrna_7sl <- subset_se(hs_hisat_mrna, subset = "sample_type=='wbcs'") %>%
  set_conditions(fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "sample_type")

hs_hisat_wbc_mrna_7sl_norm <- normalize(hs_hisat_wbc_mrna_7sl, transform = "log2",
                                        convert = "cpm", norm = "quant",
                                        filter = TRUE)
hs_hisat_wbc_mrna_7sl_npca <- plot_pca(hs_hisat_wbc_mrna_7sl_norm)
hs_hisat_wbc_mrna_7sl_npca
pp(file = "images/hs_hisat_wbc_mrna_7sl_npca.png",
   image = hs_hisat_wbc_mrna_7sl_npca$plot)

hs_hisat_wbc_mrna_7sl_nb <- normalize(hs_hisat_wbc_mrna_7sl, transform = "log2",
                                      convert = "cpm", batch = "svaseq",
                                      filter = TRUE)
hs_hisat_wbc_mrna_7sl_nbpca <- plot_pca(hs_hisat_wbc_mrna_7sl_nb)
hs_hisat_wbc_mrna_7sl_nbpca
pp(file = "images/hs_hisat_wbc_mrna_7sl_nbpca.png")
hs_hisat_wbc_mrna_7sl_nbpca$plot
dev.off()
```

### Salmon

```{r}
hs_salmon_wbc_mrna_7sl <- subset_se(hs_salmon_mrna, subset = "sample_type=='wbcs'") %>%
  set_conditions(fact = "detectionparasiteby7sl") %>%
  subset_se(subset = "condition!='notapplicable'") %>%
  set_conditions(fact = "detection_type") %>%
  set_batches(fact = "sample_type")

hs_salmon_wbc_mrna_7sl_norm <- normalize(hs_salmon_wbc_mrna_7sl, transform = "log2",
                                        convert = "cpm", norm = "quant",
                                        filter = TRUE)
hs_salmon_wbc_mrna_7sl_npca <- plot_pca(hs_salmon_wbc_mrna_7sl_norm)
hs_salmon_wbc_mrna_7sl_npca
pp(file = "images/hs_salmon_wbc_mrna_7sl_npca.png",
   image = hs_salmon_wbc_mrna_7sl_npca$plot)

hs_salmon_wbc_mrna_7sl_nb <- normalize(hs_salmon_wbc_mrna_7sl, transform = "log2",
                                      convert = "cpm", batch = "svaseq",
                                      filter = TRUE)
hs_salmon_wbc_mrna_7sl_nbpca <- plot_pca(hs_salmon_wbc_mrna_7sl_nb)
hs_salmon_wbc_mrna_7sl_nbpca
pp(file = "images/hs_salmon_wbc_mrna_7sl_nbpca.png",
   image = hs_salmon_wbc_mrna_7sl_nbpca$plot)
```

```{r}
hs_salmon_wbc_mrna_7sl_de <- all_pairwise(hs_salmon_wbc_mrna_7sl, model_fstring = "~ 0 + condition",
                                          model_svs = "svaseq", filter = TRUE, force = TRUE)
hs_salmon_wbc_mrna_7sl_de

hs_salmon_wbc_mrna_7sl_table <- combine_de_tables(
  hs_salmon_wbc_mrna_7sl_de,
  excel = glue("excel/de_tables/hs_salmon_wbc_mrna_7sl_table-v{ver}.xlsx"))
hs_salmon_wbc_mrna_7sl_table
hs_salmon_wbc_mrna_7sl_sig <- extract_significant_genes(
  hs_salmon_wbc_mrna_7sl_table,
  excel = glue("excel/sig_tables/hs_salmon_wbc_mrna_7sl_sig-v{ver}.xlsx"))
hs_salmon_wbc_mrna_7sl_sig
```

# Healthy vs Scar samples

One query from our last meeting which I forgot about until I reread my
TODO notes: compare the samples marked as healthy compared to those
marked as scar.  These are two distantly separate skin biopsies of the
same person.

```{r}
hs_hisat_skin_mrna_7sl <- subset_se(hs_hisat_mrna, subset = "hs=='scar'|hs=='healthy'")
hs_hisat_scarvs_mrna <- set_conditions(hs_hisat_skin_mrna_7sl, fact = "hs")

hs_hisat_scarvs_de <- all_pairwise(hs_hisat_scarvs_mrna, filter = TRUE,
                                   model_svs = "svaseq", model_fstring = "~ 0 + condition")
hs_hisat_scarvs_de
hs_hisat_scarvs_table <- combine_de_tables(
  hs_hisat_scarvs_de,
  excel = glue("excel/de_tables/hs_hisat_scarvs_mrna_table-v{ver}.xlsx"))
hs_hisat_scarvs_table
pp(file = "images/hisat_scarvs_deseq_genes.png", image = hs_hisat_scarvs_table[["plots"]][["scar_vs_nonlesion"]][["deseq_vol_plots"]])
hs_hisat_scarvs_sig <- extract_significant_genes(
  hs_hisat_scarvs_table,
  excel = glue("excel/sig_tables/hs_hisat_scarvs_mrna_sig-v{ver}.xlsx"))
hs_hisat_scarvs_sig
```

# Take a peek at the kraken results

```{r}
hs_kraken_viral <- create_se(pre_meta[["new_meta"]], file_column = "kraken_matrix_viral",
                             handle_na = "zero")
hs_kraken_viral <- set_conditions(hs_kraken_viral, fact = "sample_type") %>%
  set_batches("detectionparasiteby7sl")

kraken_viral_norm <- normalize(hs_kraken_viral, filter = TRUE, norm = "cpm", transform = "log2")
plot_corheat(kraken_viral_norm)
plot_disheat(kraken_viral_norm)
plot_pca(kraken_viral_norm)

nasal_kraken_viral <- subset_se(hs_kraken_viral, subset = "condition=='nasal swab'")
nasal_norm <- normalize(nasal_kraken_viral, filter = TRUE, norm = "cpm", transform = "log2")
plot_corheat(nasal_norm)

hs_kraken_bacteria <- create_se(pre_meta[["new_meta"]], file_column = "kraken_matrix_bacterial",
                                handle_na = "zero")
hs_kraken_bacteria <- set_conditions(hs_kraken_bacteria, fact = "sample_type") %>%
  set_batches("detectionparasiteby7sl")

kraken_bacteria_norm <- normalize(hs_kraken_bacteria, filter = TRUE,
                                  norm = "cpm", transform = "log2")
plot_corheat(kraken_bacteria_norm)
plot_disheat(kraken_bacteria_norm)
plot_pca(kraken_bacteria_norm)
```

# Nasal as a proxy for everything else

In the beginning of this document, I created a peculiar factor out of
the nasal sample 7SL state and applied its result to every other
sample for each person; thus a person who was positive for the nasal
sample was deemed positive for everything.  Let us see what that looks
like...

```{r}
nasal_7sl_salmon_se <- set_conditions(hs_salmon, fact = "nasal_7sl_status")
nasal_7sl_hisat_se <- set_conditions(hs_hisat, fact = "nasal_7sl_status")

nasal_7sl_salmon_se_nb <- normalize(nasal_7sl_salmon_se, transform = "log2", convert = "cpm",
                                    filter = TRUE, batch = "svaseq")
plot_pca(nasal_7sl_salmon_se_nb)
nasal_7sl_hisat_se_nb <- normalize(nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
                                   filter = TRUE, batch = "svaseq")
plot_pca(nasal_7sl_hisat_se_nb)

nasal_7sl_salmon_de <- all_pairwise(nasal_7sl_salmon_se, filter = TRUE, force = TRUE,
                             model_svs = "svaseq", model_fstring = "~ 0 + condition")
nasal_7sl_salmon_de
nasal_7sl_hisat_de <- all_pairwise(nasal_7sl_hisat_se, filter = TRUE,
                                   model_svs = "svaseq", model_fstring = "~ 0 + condition")
nasal_7sl_hisat_de

nasal_7sl_salmon_table <- combine_de_tables(nasal_7sl_salmon_de, excel = "excel/nasal_7sl_proxy_table.xlsx")
nasal_7sl_salmon_table
nasal_7sl_hisat_table <- combine_de_tables(nasal_7sl_hisat_de, excel = "excel/nasal_7sl_hisat_proxy_table.xlsx")
nasal_7sl_hisat_table
```

Oh, Maria Adelaida was actually looking only for the PBMC samples.

```{r}
pbmc_nasal_7sl_se <- subset_se(hs_salmon, subset = "condition=='pbmcs'") %>%
  set_conditions(fact = "nasal_7sl_status")
pbmc_nasal_7sl_hisat_se <- subset_se(hs_hisat, subset = "condition=='pbmcs'") %>%
  set_conditions(fact = "nasal_7sl_status")

pbmc_nasal_hisat_norm <- normalize(pbmc_nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
                                   norm = "quant", filter = TRUE)
plot_pca(pbmc_nasal_hisat_norm)

pbmc_nasal_hisat_nb <- normalize(pbmc_nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
                                 batch = "svaseq", filter = "simple")
plot_pca(pbmc_nasal_hisat_nb)

pbmc_nasal_7sl_de <- all_pairwise(pbmc_nasal_7sl_se, filter = "simple", force = TRUE,
                                  model_svs = "svaseq", model_fstring = "~ 0 + condition")
pbmc_nasal_7sl_table <- combine_de_tables(pbmc_nasal_7sl_de, excel = "excel/pbmc_nasal_proxy.xlsx")
```

# TODO 202512

Repeat this nasal proxy test using each of the other cell types.

The factors of likely interest are: "wbcs" "nasal swab" ideally both
"skin biopsy healthy" and "skin biopsy scar" but perhaps only "skin
biopsy".

```{r}
wbc_nasal_7sl_se <- subset_se(hs_salmon, subset = "condition=='wbcs'") %>%
  set_conditions(fact = "nasal_7sl_status")
wbc_nasal_7sl_hisat_se <- subset_se(hs_hisat, subset = "condition=='wbcs'") %>%
  set_conditions(fact = "nasal_7sl_status")

wbc_nasal_hisat_norm <- normalize(wbc_nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
                                  filter = "simple", norm = "quant")
plot_pca(wbc_nasal_hisat_norm)
wbc_nasal_hisat_nb <- normalize(wbc_nasal_7sl_hisat_se, transform = "log2", convert = "cpm",
                                filter = "simple", batch = "svaseq")
plot_pca(wbc_nasal_hisat_nb)

wbc_nasal_7sl_de <- all_pairwise(wbc_nasal_7sl_se, filter = "simple", force = TRUE,
                                 model_svs = "svaseq", model_fstring = "~ 0 + condition")
wbc_nasal_7sl_table <- combine_de_tables(wbc_nasal_7sl_de, excel = "excel/wbc_nasal_proxy.xlsx")
```

# Upload count tables for Mariana

```{r}
ready <- tar_meta_column(hs_hisat, column = "hisat_count_table_hg38_115")
ready <- tar_meta_column(hs_hisat, column = "salmon_count_table_hg38_115")
```

# Explicit comparison of library types

```{r}
libtype_se <- set_conditions(hs_hisat, fact = "library_type") %>%
  set_batches(fact = "sample_type")
libtype_norm <- normalize(libtype_se, transform = "log2", convert = "cpm", norm = "quant", filter = TRUE)
pp(file = "images/libtype_norm_pca.png", image = plot_pca(libtype_norm))

libtype_nb <- normalize(libtype_se, transform = "log2", convert = "cpm", batch = "svaseq", filter = TRUE)
pp(file = "images/libtype_nb_pca.png", image = plot_pca(libtype_nb))

libtype_de <- all_pairwise(libtype_se, filter = TRUE, model_svs = "svaseq",
                           model_fstring = "~ 0 + condition")
```

# 202610: Clinical Presentation: Healthy vs not

As of 202610, we have 20 samples which were never infected and 113 which have visited the clinic and
are currently deemed healthy.  These span all the celltypes.  Let us take a moment and look at them.

```{r}
presentation_se <- set_conditions(hs_hisat, fact = "clinical_presentation")
presentation_norm <- normalize(presentation_se, filter = TRUE, convert = "cpm",
                               norm = "quant", transform = "log2")
plot_pca(presentation_norm)

presentation_nb <- normalize(presentation_se, filter = TRUE, convert = "cpm",
                              batch = "svaseq", transform = "log2")
plot_pca(presentation_nb)
```
