1 Introduction

Let us check out some new cruzi infections following the deletion of a specific gene.

I thought I also did the interrogation of the CLBrener transcriptome, but that appears untrue. I think I may have forgotten to copy the genome in place…

2 Human annotation information

I have a pretty new genome downloaded (202509), so I will (for now) just let my annotation function grab whatever it thinks is reasonable. It chose the 202410 set. Seems good to me.

hs_annot <- load_biomart_annotations()
## The biomart annotations file already exists, loading from it.
tc_annot <- load_gff_annotations("~/libraries/genome/gff/tcruzi_all.gff",
                                 type = "mRNA", id_col = "Parent")
## Returning a df with 24 columns and 23305 rows.
rownames(tc_annot) <- gsub(x = make.names(tc_annot[["Name"]], unique = TRUE),
                           pattern = "\\.\\d+$", replacement = "")
esmer_db <- "org.Tcruzi.CL.Brener.Esmeraldo.like.v68.eg.db"
library(esmer_db, character.only = TRUE)
## Loading required package: AnnotationDbi
## Loading required package: stats4
## Loading required package: BiocGenerics
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:hpgltools':
## 
##     annotation, annotation<-, conditions, conditions<-
## The following objects are masked from 'package:dplyr':
## 
##     combine, intersect, setdiff, union
## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
## 
##     anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call,
##     duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply,
##     Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
##     rbind, Reduce, rownames, sapply, saveRDS, setdiff, table, tapply, union, unique, unsplit,
##     which.max, which.min
## Loading required package: Biobase
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with 'browseVignettes()'. To cite
##     Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.
## Loading required package: IRanges
## Loading required package: S4Vectors
## 
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:tidyr':
## 
##     expand
## The following objects are masked from 'package:dplyr':
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##     first, rename
## The following object is masked from 'package:utils':
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##     findMatches
## The following objects are masked from 'package:base':
## 
##     expand.grid, I, unname
## 
## Attaching package: 'IRanges'
## The following object is masked from 'package:glue':
## 
##     trim
## The following objects are masked from 'package:dplyr':
## 
##     collapse, desc, slice
## 
## Attaching package: 'AnnotationDbi'
## The following object is masked from 'package:dplyr':
## 
##     select
## 
esmer_db <- get0(esmer_db)
all_keytypes <- keytypes(esmer_db)
wanted_idx <- grepl(x = all_keytypes, pattern = "^ANNOT_")
wanted_fields <- all_keytypes[wanted_idx]
nonesmer_db <- "org.Tcruzi.CL.Brener.Non.Esmeraldo.like.v68.eg.db"
unas_db <- "org.Tcruzi.CL.Brener.v68.eg.db"

tc_esmer <- load_orgdb_annotations(esmer_db, keytype = "gid", fields = wanted_fields)
## 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 columns
tc_nonesmer <- load_orgdb_annotations(nonesmer_db, keytype = "gid", fields = wanted_fields)
## 
## 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 columns
tc_unas <- load_orgdb_annotations(unass_db, keytype = "gid", fields = wanted_fields)
## Error: object 'unass_db' not found
tc_more <- rbind(tc_esmer$genes, tc_nonesmer$genes, tc_more$genes)
## Error: object 'tc_more' not found
tc_annot <- merge(tc_annot, tc_more, by = "row.names")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'y' in selecting a method for function 'merge': object 'tc_more' not found
rownames(tc_annot) <- tc_annot[["gid"]]
tc_annot[["gid"]] <- NULL

2.1 Load cruzi GO data similarly

tc_esmer_go <- load_orgdb_go(esmer_db, keytype = "GID")
## This is an orgdb, good.
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
tc_nonesmer_go <- load_orgdb_go(nonesmer_db, keytype = "GID")
## This is an orgdb, good.
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
tc_unas_go <- load_orgdb_go(unas_db, keytype = "GID")
## 
## This is an orgdb, good.
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
tc_go <- rbind(tc_esmer_go, tc_nonesmer_go, tc_unas_go)
tc_go <- tc_go[, c("GO", "GID")]
colnames(tc_go) <- c("GO", "ID")

3 Sample sheet

I asked for one from Najib/Amalie but unless I am mistaken it has not arrived. That is not a problem, given two helpful things: April provides one, I also named the directories so that the sample IDs are built in; so I will just make a fake one for now and then merge in whatever I get from them…

sample_sheet <- "sample_sheets/all_samples.xlsx"

plot_meta_sankey(as.data.frame(extract_metadata(sample_sheet)), factors = c("background", "exp_number"))
## 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: 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 every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## A sankey plot describing the metadata of 14 samples,
## including 19 out of 0 nodes and traversing metadata factors:
## background, exp_number.

4 Adding some metadata

Let us see how well my preprocess gatherer does…

new_meta <- gather_preprocessing_metadata(sample_sheet, species = c("hg38_115", "tcruzi_all"))
## 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.
## Error in dispatch_regex_search(meta, search, replace, input_file_spec, : object 'found' not found
head(new_meta$new_meta)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'new_meta' not found

5 The primary data structure

hs_se <- create_se(new_meta[["new_meta"]], gene_info = hs_annot[["gene_annotations"]],
                   file_column = "hisat_count_table_hg38_115") %>%
  set_conditions(fact = "background") %>%
  set_batches(fact = "exp_number")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'exp' in selecting a method for function 'set_batches': error in evaluating the argument 'exp' in selecting a method for function 'set_conditions': object 'new_meta' not found
tc_se <- create_se(new_meta[["new_meta"]], gene_info = tc_annot,
                   file_column = "hisat_count_table_tcruzi_all") %>%
  set_conditions(fact = "background") %>%
  set_batches(fact = "exp_number")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'exp' in selecting a method for function 'set_batches': error in evaluating the argument 'exp' in selecting a method for function 'set_conditions': object 'new_meta' not found
tc_se <- subset_se(tc_se, nonzero = 5000)
## Error: object 'tc_se' not found
plot_libsize(hs_se)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_libsize': object 'hs_se' not found
plot_nonzero(hs_se)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_nonzero': object 'hs_se' not found
plot_libsize(tc_se)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_libsize': object 'tc_se' not found
plot_nonzero(tc_se)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_nonzero': object 'tc_se' not found
hs_replicated <- subset_se(hs_se, min_replicates = 3, fact = "condition")
## Error: object 'hs_se' not found
tc_replicated <- subset_se(tc_se, min_replicates = 3, fact = "condition") %>%
  subset_se(nonzero = 10000)
## Error: object 'tc_se' not found

6 Sample clustering

6.1 Human

devtools::load_all("~/hpgltools")
## ℹ Loading hpgltools
## in method for 'plot_topn_gsea' with signature 'gse="all_cprofiler"': no definition for class "all_cprofiler"
## 
## in method for 'plot_topn_gsea' with signature 'gse="clusterprofiler_result"': no definition for class "clusterprofiler_result"
## Warning: multiple methods tables found for 'annotation'
## Warning: multiple methods tables found for 'annotation<-'
hs_norm <- normalize(hs_replicated, transform = "log2", convert = "cpm",
                        norm = "quant", filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'normalize': object 'hs_replicated' not found
plot_disheat(hs_norm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'input_data' in selecting a method for function 'plot_heatmap': object 'hs_norm' not found
plot_corheat(hs_norm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'input_data' in selecting a method for function 'plot_heatmap': object 'hs_norm' not found
plot_pca(hs_norm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_pca': object 'hs_norm' not found
hs_rnorm <- normalize(hs_replicated, transform = "log2", convert = "cpm",
                      norm = "quant", filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'normalize': object 'hs_replicated' not found
plot_disheat(hs_rnorm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'input_data' in selecting a method for function 'plot_heatmap': object 'hs_rnorm' not found
plot_pca(hs_rnorm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_pca': object 'hs_rnorm' not found
hs_rbnorm <- normalize(hs_replicated, transform = "log2", convert = "cpm",
                       filter = TRUE, batch = "svaseq")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'normalize': object 'hs_replicated' not found
plot_pca(hs_rbnorm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_pca': object 'hs_rbnorm' not found

6.2 Parasite

tc_norm <- normalize(tc_replicated, transform = "log2", convert = "cpm",
                     norm = "quant", filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'normalize': object 'tc_replicated' not found
plot_disheat(tc_norm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'input_data' in selecting a method for function 'plot_heatmap': object 'tc_norm' not found
plot_corheat(tc_norm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'input_data' in selecting a method for function 'plot_heatmap': object 'tc_norm' not found
plot_pca(tc_norm, plot_labels = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_pca': object 'tc_norm' not found
tc_rnorm <- normalize(tc_se, transform = "log2", convert = "cpm",
                      norm = "quant", filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'normalize': object 'tc_se' not found
plot_disheat(tc_rnorm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'input_data' in selecting a method for function 'plot_heatmap': object 'tc_rnorm' not found
plot_pca(tc_rnorm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_pca': object 'tc_rnorm' not found
tc_rbnorm <- normalize(tc_se, transform = "log2", convert = "cpm",
                       filter = TRUE, batch = "svaseq")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'normalize': object 'tc_se' not found
plot_pca(tc_rbnorm)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_pca': object 'tc_rbnorm' not found

7 DE?

I am not thinking we will see many genes of interest.

hs_keepers <- list(
  "ab_vs_control" = c("AB10", "control"),
  "ko_vs_control" = c("ko7", "control"),
  "ko_vs_wt" = c("ko7", "wt"),
  "ab_vs_wt" = c("AB10", "wt"),
  "ab_vs_ko" = c("AB10", "ko7"))
hs_de <- all_pairwise(hs_replicated, filter = TRUE, model_fstring = "~ 0 + condition",
                      model_svs = "svaseq")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_replicated' not found
hs_de
## Error: object 'hs_de' not found
hs_tables <- combine_de_tables(hs_de, keepers = hs_keepers, excel = "excel/hs_tables.xlsx")
## Deleting the file excel/hs_tables.xlsx before writing the tables.
## Error: object 'hs_de' not found
hs_tables
## Error: object 'hs_tables' not found
hs_sig <- extract_significant_genes(hs_tables, excel = "excel/hs_sig.xlsx")
## Error: object 'hs_tables' not found
hs_sig
## Error: object 'hs_sig' not found
conditions(tc_replicated)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'exp' in selecting a method for function 'conditions': object 'tc_replicated' not found
tc_keepers <- list(
  "ab_vs_wt" = c("AB10", "wt"),
  "ko_vs_wt" = c("ko7", "wt"),
  "ab_vs_ko" = c("AB10", "ko7"))
tc_de <- all_pairwise(tc_replicated, filter = TRUE, model_fstring = "~ 0 + condition",
                      model_svs = "svaseq")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'tc_replicated' not found
tc_de
## Error: object 'tc_de' not found
tc_tables <- combine_de_tables(tc_de, keepers = tc_keepers, excel = "excel/tc_tables.xlsx")
## Error: object 'tc_de' not found
tc_tables
## Error: object 'tc_tables' not found
tc_sig <- extract_significant_genes(tc_tables, excel = "excel/tc_sig.xlsx")
## Error: object 'tc_tables' not found
tc_sig
## Error: object 'tc_sig' not found

8 Try some ontology searching via clusterProfiler

ko_wt_up <- tc_sig[["deseq"]][["ups"]][["ko_vs_wt"]]
## Error: object 'tc_sig' not found
ko_wt_all <- tc_tables[["data"]][["ko_vs_wt"]]
## Error: object 'tc_tables' not found
tc_esmer_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = esmer_db, orgdb_to = "GID",
  organism = "tcruzi")
## Error: object 'ko_wt_all' not found
tc_nonesmer_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = nonesmer_db, orgdb_to = "GID",
  organism = "tcruzi")
## Error: object 'ko_wt_all' not found
tc_unas_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = unas_db, orgdb_to = "GID",
  organism = "tcruzi")
## Error: object 'ko_wt_all' not found
length_db <- as.data.frame(rowData(tc_se))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': error in evaluating the argument 'x' in selecting a method for function 'rowData': object 'tc_se' not found
length_db[["gid"]] <- rownames(length_db)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'length_db' not found
length_db <- length_db[, c("gid", "width")]
## Error: object 'length_db' not found
tc_up_gs <- simple_goseq(ko_wt_up, go_db = tc_go, length_db = length_db, min_xref = 10)
## Error: object 'ko_wt_up' not found
mf_enr <- tc_up_gs[["mf_enrich"]]
## Error: object 'tc_up_gs' not found
mf_plots <- plot_enrichresult(mf_enr)
## Error: object 'mf_enr' not found
mf_plots[["tree"]]
## Error: object 'mf_plots' not found
bp_enr <- tc_up_gs[["bp_enrich"]]
## Error: object 'tc_up_gs' not found
bp_plots <- plot_enrichresult(bp_enr)
## Error: object 'bp_enr' not found
bp_plots[["dot"]]
## Error: object 'bp_plots' not found
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
message(paste0("Saving to ", savefile))
tmp <- sm(saveme(filename = savefile))
tmp <- loadme(filename = savefile)
---
title: "Examining some cruzi infected HeLa cells."
author: "atb abelew@gmail.com"
bibliography: /home/trey/scratch/zotero_library/atb.bib
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
---


```{r options, include = FALSE}
library(dplyr)
library(forcats)
library(glue)
library(hpgltools)
library(tidyr)

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(digits = 4, stringsAsFactors = FALSE, knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
ver <- Sys.getenv("VERSION")
rundate <- format(Sys.Date(), format = "%Y%m%d")

rmd_file <- "index.Rmd"
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)
methods <- list("basic" = TRUE, "deseq" = TRUE, "dream" = FALSE,
                "ebseq" = FALSE, "edger" = TRUE, "limma" = TRUE, "noiseq" = TRUE)
data_structures <- c("methods")
```

# Introduction

Let us check out some new cruzi infections following the deletion of a specific gene.

I thought I also did the interrogation of the CLBrener transcriptome,
but that appears untrue.  I think I may have forgotten to copy the
genome in place...

# Human annotation information

I have a pretty new genome downloaded (202509), so I will (for now)
just let my annotation function grab whatever it thinks is reasonable.
It chose the 202410 set.  Seems good to me.

```{r}
hs_annot <- load_biomart_annotations()

tc_annot <- load_gff_annotations("~/libraries/genome/gff/tcruzi_all.gff",
                                 type = "mRNA", id_col = "Parent")
rownames(tc_annot) <- gsub(x = make.names(tc_annot[["Name"]], unique = TRUE),
                           pattern = "\\.\\d+$", replacement = "")
esmer_db <- "org.Tcruzi.CL.Brener.Esmeraldo.like.v68.eg.db"
library(esmer_db, character.only = TRUE)
esmer_db <- get0(esmer_db)
all_keytypes <- keytypes(esmer_db)
wanted_idx <- grepl(x = all_keytypes, pattern = "^ANNOT_")
wanted_fields <- all_keytypes[wanted_idx]
nonesmer_db <- "org.Tcruzi.CL.Brener.Non.Esmeraldo.like.v68.eg.db"
unas_db <- "org.Tcruzi.CL.Brener.v68.eg.db"

tc_esmer <- load_orgdb_annotations(esmer_db, keytype = "gid", fields = wanted_fields)
tc_nonesmer <- load_orgdb_annotations(nonesmer_db, keytype = "gid", fields = wanted_fields)
tc_unas <- load_orgdb_annotations(unass_db, keytype = "gid", fields = wanted_fields)
tc_more <- rbind(tc_esmer$genes, tc_nonesmer$genes, tc_more$genes)
tc_annot <- merge(tc_annot, tc_more, by = "row.names")
rownames(tc_annot) <- tc_annot[["gid"]]
tc_annot[["gid"]] <- NULL
```

## Load cruzi GO data similarly

```{r}
tc_esmer_go <- load_orgdb_go(esmer_db, keytype = "GID")
tc_nonesmer_go <- load_orgdb_go(nonesmer_db, keytype = "GID")
tc_unas_go <- load_orgdb_go(unas_db, keytype = "GID")

tc_go <- rbind(tc_esmer_go, tc_nonesmer_go, tc_unas_go)
tc_go <- tc_go[, c("GO", "GID")]
colnames(tc_go) <- c("GO", "ID")
```

# Sample sheet

I asked for one from Najib/Amalie but unless I am mistaken it has not
arrived.  That is not a problem, given two helpful things: April
provides one, I also named the directories so that the sample IDs are
built in; so I will just make a fake one for now and then merge in
whatever I get from them...

```{r}
sample_sheet <- "sample_sheets/all_samples.xlsx"

plot_meta_sankey(as.data.frame(extract_metadata(sample_sheet)), factors = c("background", "exp_number"))
```

# Adding some metadata

Let us see how well my preprocess gatherer does...

```{r}
new_meta <- gather_preprocessing_metadata(sample_sheet, species = c("hg38_115", "tcruzi_all"))
head(new_meta$new_meta)
```

# The primary data structure

```{r}
hs_se <- create_se(new_meta[["new_meta"]], gene_info = hs_annot[["gene_annotations"]],
                   file_column = "hisat_count_table_hg38_115") %>%
  set_conditions(fact = "background") %>%
  set_batches(fact = "exp_number")

tc_se <- create_se(new_meta[["new_meta"]], gene_info = tc_annot,
                   file_column = "hisat_count_table_tcruzi_all") %>%
  set_conditions(fact = "background") %>%
  set_batches(fact = "exp_number")

tc_se <- subset_se(tc_se, nonzero = 5000)

plot_libsize(hs_se)
plot_nonzero(hs_se)

plot_libsize(tc_se)
plot_nonzero(tc_se)

hs_replicated <- subset_se(hs_se, min_replicates = 3, fact = "condition")
tc_replicated <- subset_se(tc_se, min_replicates = 3, fact = "condition") %>%
  subset_se(nonzero = 10000)
```

# Sample clustering

## Human

```{r}
devtools::load_all("~/hpgltools")
hs_norm <- normalize(hs_replicated, transform = "log2", convert = "cpm",
                        norm = "quant", filter = TRUE)
plot_disheat(hs_norm)
plot_corheat(hs_norm)
plot_pca(hs_norm)

hs_rnorm <- normalize(hs_replicated, transform = "log2", convert = "cpm",
                      norm = "quant", filter = TRUE)
plot_disheat(hs_rnorm)
plot_pca(hs_rnorm)

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

## Parasite

```{r}
tc_norm <- normalize(tc_replicated, transform = "log2", convert = "cpm",
                     norm = "quant", filter = TRUE)
plot_disheat(tc_norm)
plot_corheat(tc_norm)
plot_pca(tc_norm, plot_labels = TRUE)

tc_rnorm <- normalize(tc_se, transform = "log2", convert = "cpm",
                      norm = "quant", filter = TRUE)
plot_disheat(tc_rnorm)
plot_pca(tc_rnorm)

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

# DE?

I am not thinking we will see many genes of interest.

```{r}
hs_keepers <- list(
  "ab_vs_control" = c("AB10", "control"),
  "ko_vs_control" = c("ko7", "control"),
  "ko_vs_wt" = c("ko7", "wt"),
  "ab_vs_wt" = c("AB10", "wt"),
  "ab_vs_ko" = c("AB10", "ko7"))
hs_de <- all_pairwise(hs_replicated, filter = TRUE, model_fstring = "~ 0 + condition",
                      model_svs = "svaseq")
hs_de
hs_tables <- combine_de_tables(hs_de, keepers = hs_keepers, excel = "excel/hs_tables.xlsx")
hs_tables
hs_sig <- extract_significant_genes(hs_tables, excel = "excel/hs_sig.xlsx")
hs_sig
```


```{r}
conditions(tc_replicated)
tc_keepers <- list(
  "ab_vs_wt" = c("AB10", "wt"),
  "ko_vs_wt" = c("ko7", "wt"),
  "ab_vs_ko" = c("AB10", "ko7"))
tc_de <- all_pairwise(tc_replicated, filter = TRUE, model_fstring = "~ 0 + condition",
                      model_svs = "svaseq")
tc_de
tc_tables <- combine_de_tables(tc_de, keepers = tc_keepers, excel = "excel/tc_tables.xlsx")
tc_tables
tc_sig <- extract_significant_genes(tc_tables, excel = "excel/tc_sig.xlsx")
tc_sig
```

# Try some ontology searching via clusterProfiler


```{r}
ko_wt_up <- tc_sig[["deseq"]][["ups"]][["ko_vs_wt"]]
ko_wt_all <- tc_tables[["data"]][["ko_vs_wt"]]

tc_esmer_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = esmer_db, orgdb_to = "GID",
  organism = "tcruzi")
tc_nonesmer_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = nonesmer_db, orgdb_to = "GID",
  organism = "tcruzi")
tc_unas_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = unas_db, orgdb_to = "GID",
  organism = "tcruzi")

length_db <- as.data.frame(rowData(tc_se))
length_db[["gid"]] <- rownames(length_db)
length_db <- length_db[, c("gid", "width")]
tc_up_gs <- simple_goseq(ko_wt_up, go_db = tc_go, length_db = length_db, min_xref = 10)
mf_enr <- tc_up_gs[["mf_enrich"]]
mf_plots <- plot_enrichresult(mf_enr)
mf_plots[["tree"]]
bp_enr <- tc_up_gs[["bp_enrich"]]
bp_plots <- plot_enrichresult(bp_enr)
bp_plots[["dot"]]
```


```{r saveme, eval=FALSE}
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
message(paste0("Saving to ", savefile))
tmp <- sm(saveme(filename = savefile))
```

```{r loadme_after, eval=FALSE}
tmp <- loadme(filename = savefile)
```
