1 TODO

1.1 202512

Define a set of consistent colors. I think have darker shades for the human, but the same colors for both.
Define a dataset which includes our previous CL-Brener/CL-14 data.
We should receive some metadata including infection numbers (particularly for experiment #3), make use of this.
Define a consistent naming scheme. (condition_batch perhaps)
Define some expected numbers of expressed genes for different human/mammalian cell types. This experiment is HeLa, but I think it would be a nice bit of context to explicitly see how it compares to other organisms/cell types.
Add an outlier gene labeler for boxplots and/or print a table of outliers in plot_boxplot().
Figure out some good metrics to see if the number of not-observed genes is relevant to the other results. (plot_prepost is one possibility)
Plot coefficient of variance vs. batch/condition/etc.
Run variance partition
Note: 16 specific genes were knocked out via the addition of PTCs, make use of our freebayes/etc tools to find/quantify them.
Once we have the combined experiment, check batch #3 for how it looks with respect to other timepoints.
Make sure the Cas samples are gone after early plot(s).
Perform DE with BiM/sva/combat, compare the results.
Check/clean multigene families.
Consider with/out batch #3
On the way to that, perform comparisons of batch 3 vs. batch 1/2; perhaps the results will tell us about the batch.
Use kraken to see if there are reads which explain the difference between batch 3 and 1/2. E.g. is there any contamination? We can mostly assume there is not because of the change in human reads.
To that end, provide an explicit ratio of reads/readsmapped/etc for hs/tc or tc/hs

2 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…

3 Trans-sialidase genes which were modified

We received an email flagging the following genes as CRISPR/Cas9 targets for the knockouts. I therefore would like to have screenshots of each of these regions to show what differences are observable between the three strains. Note that the lower coverage of the last few samples may mean that we need to stick to the first group.

TcCLB.508173.120 Has putative GPI signal
TcCLB.509495.30 Has putative GPI and SAPA repeat
TcCLB.510055.20 GPI
TcCLB.506961.25 GPI ‘repeats but might not be sapa’
TcCLB.510787.10 GPI ‘sapa repeats’
TcCLB.511667.30 gpi
TcCLB.507085.30 gpi, highlighted green tyrosine, ‘sapa repeats’
TcCLB.507427.10 gpi
TcCLB.508913.25 gpi
TcCLB.508857.30 gpi
TcCLB.503993.10 gpi
TcCLB.511323.10 gpi ‘sapa repeats’
TcCLB.508089.10 gpi
TcCLB.508717.60 gpi
TcCLB.506975.80 gpi
TcCLB.505931.30 gpi
TcCLB.507979.30 gpi
TcCLB.509817.50 gpi
TcCLB.506841.20 gpi

expected_lower <- c("TcCLB.508173.120", "TcCLB.509495.30", "TcCLB.510055.20", "TcCLB.506961.25",
                    "TcCLB.510787.10", "TcCLB.511667.30", "TcCLB.507085.30", "TcCLB.507427.10",
                    "TcCLB.508913.25", "TcCLB.508857.30", "TcCLB.503993.10", "TcCLB.511323.10",
                    "TcCLB.508089.10", "TcCLB.508717.60", "TcCLB.506975.80", "TcCLB.505931.30",
                    "TcCLB.507979.30", "TcCLB.509817.50", "TcCLB.506841.20")

4 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<-, 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':
## 
##     first, rename

## The following object is masked from 'package:utils':
## 
##     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(unas_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_more <- rbind(tc_esmer$genes, tc_nonesmer$genes, tc_unas$genes)
tc_annot <- merge(tc_annot, tc_more, by = "row.names")
rownames(tc_annot) <- tc_annot[["gid"]]
tc_annot[["gid"]] <- NULL
dim(tc_annot)

## [1] 23304   135

4.1 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")

5 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.

6 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.

## Warning in dispatch_regex_search(meta, search, replace, input_file_spec, : NAs introduced by coercion

## Writing new metadata to: sample_sheets/all_samples_modified.xlsx

## Deleting the file sample_sheets/all_samples_modified.xlsx before writing the tables.

head(new_meta$new_meta)

##                                 sampleid samplenumber celltype background  hpi exp_number amount_in_10ul
## X02_HeLa_control_60h 02_HeLa_control_60h            2     HeLa    control t60h         e1            183
## X04_HeLa_WT_60hpi       04_HeLa_WT_60hpi            4     HeLa         wt t60h         e1            304
## X06_HeLa_KO7_60hpi     06_HeLa_KO7_60hpi            6     HeLa        ko7 t60h         e1            298
## X08_HeLa_Cas_60hpi     08_HeLa_Cas_60hpi            8     HeLa        cas t60h         e1            284
## X18_HeLa_control_60h 18_HeLa_control_60h           18     HeLa    control t60h         e2             62
## X20_HeLa_WT_60hpi       20_HeLa_WT_60hpi           20     HeLa         wt t60h         e2            228
##                      amount_fact condition     batch     sampleid_backup trimomatic_input
## X02_HeLa_control_60h         low undefined undefined 02_HeLa_control_60h         34421670
## X04_HeLa_WT_60hpi            mid undefined undefined    04_HeLa_WT_60hpi         33338315
## X06_HeLa_KO7_60hpi           mid undefined undefined   06_HeLa_KO7_60hpi         36904955
## X08_HeLa_Cas_60hpi           mid undefined undefined   08_HeLa_Cas_60hpi         34230672
## X18_HeLa_control_60h         low undefined undefined 18_HeLa_control_60h         31154298
## X20_HeLa_WT_60hpi            mid undefined undefined    20_HeLa_WT_60hpi         35726918
##                      trimomatic_output trimomatic_percent fastqc_pct_gc kraken_bacterial_classified
## X02_HeLa_control_60h          31723102              0.922            52                      147699
## X04_HeLa_WT_60hpi             30831462              0.925            50                      285754
## X06_HeLa_KO7_60hpi            34168992              0.926            50                      414463
## X08_HeLa_Cas_60hpi            30953413              0.904            50                      309973
## X18_HeLa_control_60h          28104898              0.902            51                      147359
## X20_HeLa_WT_60hpi             32916331              0.921            50                      323491
##                      kraken_bacterial_unclassified kraken_first_bacterial_species
## X02_HeLa_control_60h                        418871        Porphyrobacter sp. GA68
## X04_HeLa_WT_60hpi                          6263711        Mycoplasmopsis arginini
## X06_HeLa_KO7_60hpi                         8109109        Mycoplasmopsis arginini
## X08_HeLa_Cas_60hpi                         7277804        Mycoplasmopsis arginini
## X18_HeLa_control_60h                        374703        Porphyrobacter sp. GA68
## X20_HeLa_WT_60hpi                          8424975          Klebsiella pneumoniae
##                      kraken_first_bacterial_species_reads
## X02_HeLa_control_60h                                34515
## X04_HeLa_WT_60hpi                                   20649
## X06_HeLa_KO7_60hpi                                  95574
## X08_HeLa_Cas_60hpi                                  22086
## X18_HeLa_control_60h                                22324
## X20_HeLa_WT_60hpi                                    4599
##                                                                                         kraken_matrix_bacterial
## X02_HeLa_control_60h preprocessing/02_HeLa_control_60h/outputs/20251031kraken_bacteria/kraken_report_matrix.tsv
## X04_HeLa_WT_60hpi       preprocessing/04_HeLa_WT_60hpi/outputs/20251031kraken_bacteria/kraken_report_matrix.tsv
## X06_HeLa_KO7_60hpi     preprocessing/06_HeLa_KO7_60hpi/outputs/20251031kraken_bacteria/kraken_report_matrix.tsv
## X08_HeLa_Cas_60hpi     preprocessing/08_HeLa_Cas_60hpi/outputs/20251031kraken_bacteria/kraken_report_matrix.tsv
## X18_HeLa_control_60h preprocessing/18_HeLa_control_60h/outputs/20251031kraken_bacteria/kraken_report_matrix.tsv
## X20_HeLa_WT_60hpi       preprocessing/20_HeLa_WT_60hpi/outputs/20251031kraken_bacteria/kraken_report_matrix.tsv
##                      hisat_genome_input_reads_hg38_115 hisat_genome_input_reads_tcruzi_all
## X02_HeLa_control_60h                          31723102                              566570
## X04_HeLa_WT_60hpi                             30831462                             6549465
## X06_HeLa_KO7_60hpi                                  NA                             8523572
## X08_HeLa_Cas_60hpi                            30953413                             7587777
## X18_HeLa_control_60h                          28104898                              522062
## X20_HeLa_WT_60hpi                             32916331                             8748466
##                      hisat_genome_single_concordant_hg38_115 hisat_genome_single_concordant_tcruzi_all
## X02_HeLa_control_60h                                27374698                                       533
## X04_HeLa_WT_60hpi                                   21550886                                   3958505
## X06_HeLa_KO7_60hpi                                        NA                                   5187623
## X08_HeLa_Cas_60hpi                                  20831115                                   4581882
## X18_HeLa_control_60h                                24646849                                      1779
## X20_HeLa_WT_60hpi                                   21560373                                   5359267
##                      hisat_genome_multi_concordant_hg38_115 hisat_genome_multi_concordant_tcruzi_all
## X02_HeLa_control_60h                                3781834                                      148
## X04_HeLa_WT_60hpi                                   2731111                                  1769606
## X06_HeLa_KO7_60hpi                                       NA                                  2326419
## X08_HeLa_Cas_60hpi                                  2534521                                  2096222
## X18_HeLa_control_60h                                2935987                                      580
## X20_HeLa_WT_60hpi                                   2607492                                  2409017
##                      hisat_genome_single_all_hg38_115 hisat_genome_single_all_tcruzi_all
## X02_HeLa_control_60h                           393579                              31940
## X04_HeLa_WT_60hpi                              386791                             191224
## X06_HeLa_KO7_60hpi                                 NA                             219958
## X08_HeLa_Cas_60hpi                             370232                             198309
## X18_HeLa_control_60h                           371885                              44606
## X20_HeLa_WT_60hpi                              394781                             254343
##                      hisat_genome_multi_all_hg38_115 hisat_genome_multi_all_tcruzi_all
## X02_HeLa_control_60h                          147888                             26257
## X04_HeLa_WT_60hpi                             125185                             98192
## X06_HeLa_KO7_60hpi                                NA                            120720
## X08_HeLa_Cas_60hpi                            118754                            105444
## X18_HeLa_control_60h                          118560                             22098
## X20_HeLa_WT_60hpi                             124747                            122425
##                      hisat_unmapped_hg38_115 hisat_unmapped_tcruzi_all hisat_genome_percent_log_hg38_115
## X02_HeLa_control_60h                  485321                   1073519                             99.24
## X04_HeLa_WT_60hpi                   12501300                   1335114                             79.73
## X06_HeLa_KO7_60hpi                        NA                   1655578                                NA
## X08_HeLa_Cas_60hpi                  14599664                   1495987                             76.42
## X18_HeLa_control_60h                  474391                    972594                             99.16
## X20_HeLa_WT_60hpi                   16893802                   1557970                             74.34
##                      hisat_genome_percent_log_tcruzi_all
## X02_HeLa_control_60h                                5.26
## X04_HeLa_WT_60hpi                                  89.81
## X06_HeLa_KO7_60hpi                                 90.29
## X08_HeLa_Cas_60hpi                                 90.14
## X18_HeLa_control_60h                                6.85
## X20_HeLa_WT_60hpi                                  91.10
##                                                                                  hisat_alignment_hg38_115
## X02_HeLa_control_60h preprocessing/02_HeLa_control_60h/outputs/20251031hisat_hg38_115/hg38_115_genome.bam
## X04_HeLa_WT_60hpi       preprocessing/04_HeLa_WT_60hpi/outputs/20251031hisat_hg38_115/hg38_115_genome.bam
## X06_HeLa_KO7_60hpi     preprocessing/06_HeLa_KO7_60hpi/outputs/20251031hisat_hg38_115/hg38_115_genome.bam
## X08_HeLa_Cas_60hpi     preprocessing/08_HeLa_Cas_60hpi/outputs/20251031hisat_hg38_115/hg38_115_genome.bam
## X18_HeLa_control_60h preprocessing/18_HeLa_control_60h/outputs/20251031hisat_hg38_115/hg38_115_genome.bam
## X20_HeLa_WT_60hpi       preprocessing/20_HeLa_WT_60hpi/outputs/20251031hisat_hg38_115/hg38_115_genome.bam
##                                                                                    hisat_alignment_tcruzi_all
## X02_HeLa_control_60h preprocessing/02_HeLa_control_60h/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome.bam
## X04_HeLa_WT_60hpi       preprocessing/04_HeLa_WT_60hpi/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome.bam
## X06_HeLa_KO7_60hpi     preprocessing/06_HeLa_KO7_60hpi/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome.bam
## X08_HeLa_Cas_60hpi     preprocessing/08_HeLa_Cas_60hpi/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome.bam
## X18_HeLa_control_60h preprocessing/18_HeLa_control_60h/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome.bam
## X20_HeLa_WT_60hpi       preprocessing/20_HeLa_WT_60hpi/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome.bam
##                      salmon_percent_hg38_115 salmon_percent_tcruzi_all salmon_observed_genes_hg38_115
## X02_HeLa_control_60h                   45.40                        NA                          47839
## X04_HeLa_WT_60hpi                      35.05                        NA                          46509
## X06_HeLa_KO7_60hpi                     33.15                        NA                          48117
## X08_HeLa_Cas_60hpi                     33.47                        NA                          46351
## X18_HeLa_control_60h                   43.10                        NA                          47978
## X20_HeLa_WT_60hpi                      34.33                        NA                          47985
##                      salmon_observed_genes_tcruzi_all
## X02_HeLa_control_60h                                0
## X04_HeLa_WT_60hpi                                   0
## X06_HeLa_KO7_60hpi                                  0
## X08_HeLa_Cas_60hpi                                  0
## X18_HeLa_control_60h                                0
## X20_HeLa_WT_60hpi                                   0
##                                                                  input_r1
## X02_HeLa_control_60h unprocessed/02_HeLa_control_60h_2_S1_R1_001.fastq.gz
## X04_HeLa_WT_60hpi       unprocessed/04_HeLa_WT_60hpi_2_S2_R1_001.fastq.gz
## X06_HeLa_KO7_60hpi     unprocessed/06_HeLa_KO7_60hpi_2_S3_R1_001.fastq.gz
## X08_HeLa_Cas_60hpi     unprocessed/08_HeLa_Cas_60hpi_2_S4_R1_001.fastq.gz
## X18_HeLa_control_60h unprocessed/18_HeLa_control_60h_2_S5_R1_001.fastq.gz
## X20_HeLa_WT_60hpi       unprocessed/20_HeLa_WT_60hpi_2_S6_R1_001.fastq.gz
##                                                                  input_r2
## X02_HeLa_control_60h unprocessed/02_HeLa_control_60h_2_S1_R2_001.fastq.gz
## X04_HeLa_WT_60hpi       unprocessed/04_HeLa_WT_60hpi_2_S2_R2_001.fastq.gz
## X06_HeLa_KO7_60hpi     unprocessed/06_HeLa_KO7_60hpi_2_S3_R2_001.fastq.gz
## X08_HeLa_Cas_60hpi     unprocessed/08_HeLa_Cas_60hpi_2_S4_R2_001.fastq.gz
## X18_HeLa_control_60h unprocessed/18_HeLa_control_60h_2_S5_R2_001.fastq.gz
## X20_HeLa_WT_60hpi       unprocessed/20_HeLa_WT_60hpi_2_S6_R2_001.fastq.gz
##                                                                                                             hisat_count_table_hg38_115
## X02_HeLa_control_60h preprocessing/02_HeLa_control_60h/outputs/20251031hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## X04_HeLa_WT_60hpi       preprocessing/04_HeLa_WT_60hpi/outputs/20251031hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## X06_HeLa_KO7_60hpi     preprocessing/06_HeLa_KO7_60hpi/outputs/20251031hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## X08_HeLa_Cas_60hpi     preprocessing/08_HeLa_Cas_60hpi/outputs/20251031hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## X18_HeLa_control_60h preprocessing/18_HeLa_control_60h/outputs/20251031hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
## X20_HeLa_WT_60hpi       preprocessing/20_HeLa_WT_60hpi/outputs/20251031hisat_hg38_115/hg38_115_genome-paired_s2_gene_ID_fcounts.csv.xz
##                                                                                                               hisat_count_table_tcruzi_all
## X02_HeLa_control_60h preprocessing/02_HeLa_control_60h/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome-paired_s2_gene_ID_fcounts.csv.xz
## X04_HeLa_WT_60hpi       preprocessing/04_HeLa_WT_60hpi/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome-paired_s2_gene_ID_fcounts.csv.xz
## X06_HeLa_KO7_60hpi     preprocessing/06_HeLa_KO7_60hpi/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome-paired_s2_gene_ID_fcounts.csv.xz
## X08_HeLa_Cas_60hpi     preprocessing/08_HeLa_Cas_60hpi/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome-paired_s2_gene_ID_fcounts.csv.xz
## X18_HeLa_control_60h preprocessing/18_HeLa_control_60h/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome-paired_s2_gene_ID_fcounts.csv.xz
## X20_HeLa_WT_60hpi       preprocessing/20_HeLa_WT_60hpi/outputs/20251031hisat_tcruzi_all/tcruzi_all_genome-paired_s2_gene_ID_fcounts.csv.xz
##                                                                         salmon_count_table_hg38_115
## X02_HeLa_control_60h preprocessing/02_HeLa_control_60h/outputs/20251031salmon_hg38_115_CDS/quant.sf
## X04_HeLa_WT_60hpi       preprocessing/04_HeLa_WT_60hpi/outputs/20251031salmon_hg38_115_CDS/quant.sf
## X06_HeLa_KO7_60hpi     preprocessing/06_HeLa_KO7_60hpi/outputs/20251031salmon_hg38_115_CDS/quant.sf
## X08_HeLa_Cas_60hpi     preprocessing/08_HeLa_Cas_60hpi/outputs/20251031salmon_hg38_115_CDS/quant.sf
## X18_HeLa_control_60h preprocessing/18_HeLa_control_60h/outputs/20251031salmon_hg38_115_CDS/quant.sf
## X20_HeLa_WT_60hpi       preprocessing/20_HeLa_WT_60hpi/outputs/20251031salmon_hg38_115_CDS/quant.sf
##                      salmon_count_table_tcruzi_all
## X02_HeLa_control_60h                            NA
## X04_HeLa_WT_60hpi                               NA
## X06_HeLa_KO7_60hpi                              NA
## X08_HeLa_Cas_60hpi                              NA
## X18_HeLa_control_60h                            NA
## X20_HeLa_WT_60hpi                               NA

7 Define colors

color_choices <- list(
  "hs" = list(
    "AB10" = "#086448",
    "cas" = "#702601",
    "control" = "#454178",
    "ko7" = "#870649",
    "positive" = "#46060E",
    "wt" = "#785C01"),
  "tc" = list(
    "AB10" = "#0DA877",
    "cas" = "#BA3F01",
    "control" = "#7771D1",
    "ko7" = "#BF086A",
    "positive" = "#8F0C1E",
    "wt" = "#AF8401"))

These colors are bad, the human are too dark and lose their contrast with respect to each other. I should get Najib/April/Amalie to help define better.

8 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") %>%
  set_colors(color_choices[["hs"]])

## 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: 14 rows(samples) and 44 columns(metadata fields).

## Matched 21562 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 44 columns.

## The numbers of samples by condition are:

## 
##     AB10      cas  control      ko7 positive       wt 
##        3        1        3        3        1        3

## The number of samples by batch are:

## 
##    e1    e2    e3 undef 
##     4     4     5     1

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") %>%
  set_colors(color_choices[["tc"]])

## 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: 14 rows(samples) and 44 columns(metadata fields).

## Matched 23304 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 25100 rows and 44 columns.

## The numbers of samples by condition are:

## 
##     AB10      cas  control      ko7 positive       wt 
##        3        1        3        3        1        3

## The number of samples by batch are:

## 
##    e1    e2    e3 undef 
##     4     4     5     1

9 A Few initial plots

I picked off another TODO in here, I changed plot_nonzero to more intelligently set the text annotation.

plot_nonzero(tc_se)

## The following samples have less than 16315 genes.

## [1] "02_HL_c_60" "18_HL_c_60" "34_HL_c_60" "pos_ctrl"

## 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 every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

## A non-zero genes plot of 14 samples.
## These samples have an average 1.475 CPM coverage and 15473 genes observed, ranging from 36 to
## 22625.

## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider increasing max.overlaps

tc_se <- subset_se(tc_se, nonzero = 5000)

## The samples (and read coverage) removed when filtering 5000 non-zero genes are:

## 02_HeLa_control_60h 18_HeLa_control_60h 34_HeLa_control_60h            pos_ctrl 
##                 122                 663                 107                  48 
## 02_HeLa_control_60h 18_HeLa_control_60h 34_HeLa_control_60h            pos_ctrl 
##                  81                 523                  91                  36

## Samples removed: 81, 523, 91, 36

plot_nonzero(tc_se, y_intercept = 0.9)

## 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.
## A non-zero genes plot of 10 samples.
## These samples have an average 2.064 CPM coverage and 21590 genes observed, ranging from 20421 to
## 22625.

plot_libsize(hs_se)

## Library sizes of 14 samples, 
## ranging from 14,183,768 to 29,076,224.

plot_nonzero(hs_se)

## 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.
## A non-zero genes plot of 14 samples.
## These samples have an average 20.55 CPM coverage and 15472 genes observed, ranging from 15027 to
## 17380.

## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps

plot_boxplot(hs_se)

## 85373 entries are 0.  We are on a log scale, adding 1 to the data.
## Plot describing the gene distribution from a dataset.

plot_libsize(tc_se)

## Library sizes of 10 samples, 
## ranging from 791,179 to 3,382,177.

plot_nonzero(tc_se, y_intercept = 0.9)

## 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.
## A non-zero genes plot of 10 samples.
## These samples have an average 2.064 CPM coverage and 21590 genes observed, ranging from 20421 to
## 22625.

plot_boxplot(tc_se)

## 35096 entries are 0.  We are on a log scale, adding 1 to the data.
## Plot describing the gene distribution from a dataset.

hs_replicated <- subset_se(hs_se, min_replicates = 3, fact = "condition")

## Removing samples with less than 3 replicates.
## Removed: 08_HeLa_Cas_60hpi, pos_ctrl.

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

## Removing samples with less than 3 replicates.
## Removed: 08_HeLa_Cas_60hpi.
## No samples have fewer than 10000 observed genes.

10 Variance Partition

Here are a couple of variance partition invocations. Once we have other metadata, this will be more useful. Note, this will only work once the non-replicated conditions are removed (control and cas).

hs_varpart <- simple_varpart(hs_replicated)

## The model of ~ condition + batch has 6 levels and rank 6

hs_varpart

##

hs_varpart[["percent_plot"]]

tc_varpart <- simple_varpart(tc_replicated)

## The model of ~ condition + batch has 5 levels and rank 5

## Warning in .fitExtractVarPartModel(exprObj, formula, data, REML = REML, : Model failed for 11 responses.
##   See errors with attr(., 'errors')

tc_varpart

##

tc_varpart[["percent_plot"]]

I think we probably should not be surprised at the amount of variance attributed to the batch due to the very large difference in coverage between experiment #3 and 1/2.

11 Sample clustering

11.1 Human

Perform our default PCA plot along with a combat version.

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

## Removing 9809 low-count genes (11762 remaining).

## Setting 1672 entries to zero.

plot_disheat(hs_norm)

## A heatmap of pairwise sample distances ranging from: 
## 16.2110615399822 to 91.122185751637.

plot_corheat(hs_norm)

## A heatmap of pairwise sample correlations ranging from: 
## 0.916078357207824 to 0.997343768762044.

plot_pca(hs_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 AB10, control, ko7, wt
## Shapes are defined by e1, e2, e3.

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

## Removing 9809 low-count genes (11762 remaining).

## transform_counts: Found 25 values less than 0.

## transform_counts: Found 25 values equal to 0, adding 1 to the matrix.

plot_pca(hs_nb)

## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by AB10, control, ko7, wt
## Shapes are defined by e1, e2, e3.

hs_cb <- normalize(hs_replicated, transform = "log2", convert = "cpm",
                   filter = TRUE, batch = "combat")

## Removing 9809 low-count genes (11762 remaining).

## transform_counts: Found 84 values less than 0.

## transform_counts: Found 89 values equal to 0, adding 1 to the matrix.

plot_pca(hs_cb)

## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by AB10, control, ko7, wt
## Shapes are defined by e1, e2, e3.

11.2 Parasite

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

## Removing 4102 low-count genes (20998 remaining).

## transform_counts: Found 52 values equal to 0, adding 1 to the matrix.

plot_disheat(tc_norm)

## A heatmap of pairwise sample distances ranging from: 
## 62.9109619364137 to 117.665159976867.

plot_corheat(tc_norm)

## A heatmap of pairwise sample correlations ranging from: 
## 0.905660328467753 to 0.973093779177062.

norm_boxplot <- plot_boxplot(tc_norm)
norm_boxplot[["plot"]]

high_genes <- rownames(norm_boxplot[["high_outlier_df"]])
fData(tc_se)[high_genes, ][["annot_transcript_product"]]

##  [1] "alpha tubulin, putative"                                        
##  [2] "undefined"                                                      
##  [3] "undefined"                                                      
##  [4] "undefined"                                                      
##  [5] "undefined"                                                      
##  [6] "undefined"                                                      
##  [7] "undefined"                                                      
##  [8] "Voltage-dependent calcium channel subunit, putative"            
##  [9] "enolase"                                                        
## [10] "zinc finger CCCH domain containing protein 11"                  
## [11] "60S ribosomal protein L6, putative"                             
## [12] "clathrin heavy chain, putative"                                 
## [13] "ribosomal protein l35a, putative"                               
## [14] "conserved protein"                                              
## [15] "cyclophilin a, putative"                                        
## [16] "hypothetical protein, conserved"                                
## [17] "ABC transporter, putative"                                      
## [18] "trans-sialidase, Group II, putative"                            
## [19] "ABC transporter, putative"                                      
## [20] "zinc finger CCCH domain containing protein 11"                  
## [21] "polyadenylate-binding protein, putative"                        
## [22] "metallo-peptidase, Clan MF, Family M17"                         
## [23] "ubiquitin-protein ligase-like, putative"                        
## [24] "60S ribosomal protein L19, putative"                            
## [25] "60S ribosomal protein L23a, putative"                           
## [26] "RNA-binding protein 42 (RNA-binding motif protein 42), putative"
## [27] "CCR4-NOT transcription complex subunit 1"                       
## [28] "dynein heavy chain, putative"                                   
## [29] "ABC transporter, putative"                                      
## [30] "mitochondrial RNA binding complex 1 subunit, putative"          
## [31] "60S ribosomal protein L10a, putative"                           
## [32] "hypothetical protein"                                           
## [33] "cell division cycle protein 20, putative"

plot_pca(tc_norm, plot_labels = TRUE)

## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by AB10, cas, ko7, wt
## Shapes are defined by e1, e2, e3.

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

## Removing 4164 low-count genes (20936 remaining).

## transform_counts: Found 47 values equal to 0, adding 1 to the matrix.

plot_disheat(tc_rnorm)

## A heatmap of pairwise sample distances ranging from: 
## 63.0136687923128 to 117.863511218509.

plot_pca(tc_rnorm)

## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by AB10, ko7, wt
## Shapes are defined by e1, e2, e3.

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

## Removing 4164 low-count genes (20936 remaining).

## transform_counts: Found 1252 values less than 0.

## transform_counts: Found 1252 values equal to 0, adding 1 to the matrix.

plot_pca(tc_rbnorm)

## The result of performing a fast_svd dimension reduction.
## The x-axis is PC1 and the y-axis is PC2
## Colors are defined by AB10, ko7, wt
## Shapes are defined by e1, e2, e3.

12 Differential Expression

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")

##    AB10 control     ko7      wt 
##       3       3       3       3

## Removing 9809 low-count genes (11762 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 1603 entries to zero.

## This received a matrix of SVs.

## converting counts to integer mode

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## conditions
##    AB10 control     ko7      wt 
##       3       3       3       3

## conditions
##    AB10 control     ko7      wt 
##       3       3       3       3

## conditions
##    AB10 control     ko7      wt 
##       3       3       3       3

hs_de

## 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 6 comparisons.

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.

## Looking for subscript invalid names, start of extract_keepers.

## Looking for subscript invalid names, end of extract_keepers.

hs_tables

## A set of combined differential expression results.

##                      table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 control_vs_AB10-inverted          16            29          25            43           2             1
## 2           ko7_vs_control          82            20         104            28           4             0
## 3       wt_vs_ko7-inverted           0             3           0             4           0             0
## 4      wt_vs_AB10-inverted           0             1           0             1           0             0
## 5     ko7_vs_AB10-inverted           0             9           0            16           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 to the authors.
## This warning is displayed once every 8 hours.
## 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 to the authors.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

## Plot describing unique/shared genes in a differential expression table.

hs_sig <- extract_significant_genes(hs_tables, excel = "excel/hs_sig.xlsx")

## Deleting the file excel/hs_sig.xlsx before writing the tables.

hs_sig

## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.

## The parameters defining significant were:

## LFC cutoff: 1 adj P cutoff: 0.05

##               limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up ebseq_down basic_up
## ab_vs_control        2          1       25         43       16         29       11          0        0
## ko_vs_control        4          0      104         28       82         20       25          1        0
## ko_vs_wt             0          0        0          4        0          3        0          1        0
## ab_vs_wt             0          0        0          1        0          1        0          0        0
## ab_vs_ko             0          0        0         16        0          9        0          0        0
##               basic_down
## ab_vs_control          0
## ko_vs_control          0
## ko_vs_wt               0
## ab_vs_wt               0
## ab_vs_ko               0

While it is true there are not a tremendous number of genes, at least some of the groups are interesting.

hs_gp <- all_gprofiler(hs_sig)
hs_gp

##                    BP CC CORUM HP HPA KEGG MIRNA MF REAC TF WP
## ab_vs_control_up    0  5     0  0   0    0     1  1    1  0  0
## ab_vs_control_down  4  1     0  0   0    0     0  4    0  0  0
## ko_vs_control_up   64  2     0  0   0    2     1 15    5 21  4
## ko_vs_control_down 12  5     1  0   0    1     1  8    2  0  0

plot_enrichresult(hs_gp[["ko_vs_control_up"]][["BP_enrich"]])

## Warning in fortify(object, showCategory = showCategory, by = x, ...): Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = x
## ℹ Did you misspell an argument name?

## Warning in fortify(data, ...): Arguments in `...` must be used.
## ✖ Problematic arguments:
## • as.Date = as.Date
## • yscale_mapping = yscale_mapping
## • show.legend = FALSE
## ℹ Did you misspell an argument name?

## ! # 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.
## ! # 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.
## ! # 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.

## $bar

## 
## $cnet

## 
## $dot

## 
## $go

## Warning: ggrepel: 13 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## 
## $heat

## 
## $map

## 
## $ss
## NULL
## 
## $termsim
## #
## # over-representation test
## #
## #...@organism     UNKNOWN 
## #...@ontology     BP 
## #...@gene     chr [1:82] "ENSG00000049249" "ENSG00000115008" "ENSG00000128965" "ENSG00000139269" "ENSG00000170909" ...
## #...pvalues adjusted by 'BH' with cutoff <0.05 
## #...49 enriched terms found
## 'data.frame':    49 obs. of  9 variables:
##  $ ID         : chr  "GO:0034103" "GO:0045071" "GO:0006952" "GO:0043207" ...
##  $ Description: chr  "regulation of tissue remodeling" "negative regulation of viral genome replication" "defense response" "response to external biotic stimulus" ...
##  $ GeneRatio  : chr  "6/76" "6/57" "23/1853" "21/1595" ...
##  $ BgRatio    : chr  "76/59" "57/80" "1853/80" "1595/80" ...
##  $ pvalue     : num  0.000126 0.000139 0.000417 0.000616 0.00063 ...
##  $ p.adjust   : num  0.000126 0.000139 0.000417 0.000616 0.00063 ...
##  $ qvalue     : num  0.000126 0.000139 0.000417 0.000616 0.00063 ...
##  $ geneID     : chr  "ENSG00000170909/ENSG00000136244/ENSG00000136235/ENSG00000162733/ENSG00000154175/ENSG00000118503" "ENSG00000271503/ENSG00000157601/ENSG00000115267/ENSG00000172183/ENSG00000135114/ENSG00000187608" "ENSG00000115008/ENSG00000271503/ENSG00000124882/ENSG00000073756/ENSG00000157601/ENSG00000176046/ENSG00000136244"| __truncated__ "ENSG00000115008/ENSG00000271503/ENSG00000124882/ENSG00000073756/ENSG00000157601/ENSG00000136244/ENSG00000100342"| __truncated__ ...
##  $ Count      : int  6 6 23 21 2 2 2 2 16 18 ...
## #...Citation
## G Yu. Thirteen years of clusterProfiler. The Innovation. 2024, 5(6):100722 
## 
## 
## $tree

## 
## $up

## 
## $vol
## NULL

plot_enrichresult(hs_gp[["ko_vs_control_down"]][["BP_enrich"]])

## Warning in fortify(object, showCategory = showCategory, by = x, ...): Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = x
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic arguments:
## • as.Date = as.Date
## • yscale_mapping = yscale_mapping
## • show.legend = FALSE
## ℹ Did you misspell an argument name?

## ! # 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.

## $bar

## 
## $cnet

## 
## $dot

## 
## $go

## Warning: ggrepel: 98 unlabeled data points (too many overlaps). Consider increasing max.overlaps

## 
## $heat

## 
## $map

## 
## $ss
## NULL
## 
## $termsim
## #
## # over-representation test
## #
## #...@organism     UNKNOWN 
## #...@ontology     BP 
## #...@gene     chr [1:20] "ENSG00000174600" "ENSG00000187689" "ENSG00000160201" "ENSG00000118194" "ENSG00000186340" ...
## #...pvalues adjusted by 'BH' with cutoff <0.05 
## #...11 enriched terms found
## 'data.frame':    11 obs. of  9 variables:
##  $ ID         : chr  "GO:0043567" "GO:0010286" "GO:0070370" "GO:0070434" ...
##  $ Description: chr  "regulation of insulin-like growth factor receptor signaling pathway" "heat acclimation" "cellular heat acclimation" "positive regulation of nucleotide-binding oligomerization domain containing 2 signaling pathway" ...
##  $ GeneRatio  : chr  "3/23" "2/5" "2/5" "2/6" ...
##  $ BgRatio    : chr  "23/17" "5/14" "5/14" "6/14" ...
##  $ pvalue     : num  0.0011 0.00587 0.00587 0.00881 0.01233 ...
##  $ p.adjust   : num  0.0011 0.00587 0.00587 0.00881 0.01233 ...
##  $ qvalue     : num  0.0011 0.00587 0.00587 0.00881 0.01233 ...
##  $ geneID     : chr  "ENSG00000115457/ENSG00000115461/ENSG00000017427" "ENSG00000204388/ENSG00000204389" "ENSG00000204388/ENSG00000204389" "ENSG00000204388/ENSG00000204389" ...
##  $ Count      : int  3 2 2 2 2 3 2 2 2 4 ...
## #...Citation
## G Yu. Thirteen years of clusterProfiler. The Innovation. 2024, 5(6):100722 
## 
## 
## $tree

## 
## $up

## 
## $vol
## NULL

I think it is noteworthy that there is a difference in T cell proliferation.

conditions(tc_replicated)

## Error: unable to find an inherited method for function 'conditions' for signature 'object = "SummarizedExperiment"'

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")

## AB10  ko7   wt 
##    3    3    3

## Removing 4164 low-count genes (20936 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 6919 entries to zero.

## This received a matrix of SVs.

## converting counts to integer mode

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## conditions
## AB10  ko7   wt 
##    3    3    3

## conditions
## AB10  ko7   wt 
##    3    3    3

## conditions
## AB10  ko7   wt 
##    3    3    3

tc_de

## 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 3 comparisons.

tc_tables <- combine_de_tables(tc_de, keepers = tc_keepers, excel = "excel/tc_tables.xlsx")

## Deleting the file excel/tc_tables.xlsx before writing the tables.

## Looking for subscript invalid names, start of extract_keepers.

## Looking for subscript invalid names, end of extract_keepers.

tc_tables

## A set of combined differential expression results.

##                  table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1  wt_vs_AB10-inverted          39           349         101           603          92           300
## 2   wt_vs_ko7-inverted          48            44         115           147          83            60
## 3 ko7_vs_AB10-inverted          11           259          44           428          18           220

## Plot describing unique/shared genes in a differential expression table.

tc_sig <- extract_significant_genes(tc_tables, excel = "excel/tc_sig.xlsx")

## Deleting the file excel/tc_sig.xlsx before writing the tables.

tc_sig

## A set of genes deemed significant according to limma, edger, deseq, ebseq, basic.

## The parameters defining significant were:

## LFC cutoff: 1 adj P cutoff: 0.05

##          limma_up limma_down edger_up edger_down deseq_up deseq_down ebseq_up ebseq_down basic_up
## ab_vs_wt       92        300      101        603       39        349       51        277        0
## ko_vs_wt       83         60      115        147       48         44      114         39        0
## ab_vs_ko       18        220       44        428       11        259        8        222        0
##          basic_down
## ab_vs_wt          0
## ko_vs_wt          0
## ab_vs_ko          0

13 Try some ontology searching via clusterProfiler

We cannot use gProfiler2 with the parasite because it is not a reference species; but other ontology methods are not constrained thus. In the case of clusterProfiler, there is another constraint, I do not have a single orgDB object which comprises Esmer/NonEsmer/Unassigned; as a result I must attempt the ontology search on the haplotypes separately.

ko_wt_up <- tc_sig[["deseq"]][["ups"]][["ko_vs_wt"]]
ko_wt_down <- tc_sig[["deseq"]][["downs"]][["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", excel = "excel/ko_wt_up_cp_esmer.xlsx")

## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

## preparing geneSet collections...

## GSEA analysis...

## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (31.16% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 25
## pathways for which P-values were not calculated properly due to unbalanced (positive and negative)
## gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the
## pathways the P-values were likely overestimated. For such pathways log2err is set to NA.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in
## reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.

## leading edge analysis...

## done...

## Deleting the file excel/ko_wt_up_cp_esmer.xlsx before writing the tables.

## ! # 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.
## ! # 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 in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 10, TcCLB.409625 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.

## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 10, TcCLB.409625 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.
## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 10, TcCLB.409625 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.
## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 10, TcCLB.409625 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.

## ! # 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.
## ! # 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.
## ! # 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.

tc_nonesmer_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = nonesmer_db, orgdb_to = "GID",
  organism = "tcruzi", excel = "excel/ko_wt_up_cp_nonesmer.xlsx")

## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

## preparing geneSet collections...

## GSEA analysis...

## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (30.08% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 70
## pathways for which P-values were not calculated properly due to unbalanced (positive and negative)
## gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the
## pathways the P-values were likely overestimated. For such pathways log2err is set to NA.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in
## reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.

## leading edge analysis...

## done...

## Deleting the file excel/ko_wt_up_cp_nonesmer.xlsx before writing the tables.

## ! # 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.
## ! # 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 in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, :  TcCLB.409625.10 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.

## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, :  TcCLB.409625.10 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.
## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, :  TcCLB.409625.10 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.
## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, :  TcCLB.409625.10 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.

## ! # 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.

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

## --> No gene can be mapped....

## --> Expected input gene ID: TcCLB.457101.20,TcCLB.509961.90,TcCLB.507153.10,TcCLB.510783.11,TcCLB.411235.9,TcCLB.505051.20

## --> return NULL...

## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

## preparing geneSet collections...

## GSEA analysis...

## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (67.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 34
## pathways for which P-values were not calculated properly due to unbalanced (positive and negative)
## gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the
## pathways the P-values were likely overestimated. For such pathways log2err is set to NA.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in
## reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.

## leading edge analysis...

## done...

tc_esmer_up_cp


tc_esmer_down_cp <- simple_clusterprofiler(
  ko_wt_down, de_table = ko_wt_all, orgdb = esmer_db, orgdb_to = "GID",
  organism = "tcruzi", excel = "excel/ko_wt_down_cp_esmer.xlsx")

## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

## preparing geneSet collections...

## GSEA analysis...

## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (31.16% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 25
## pathways for which P-values were not calculated properly due to unbalanced (positive and negative)
## gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the
## pathways the P-values were likely overestimated. For such pathways log2err is set to NA.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in
## reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.

## leading edge analysis...

## done...

## Deleting the file excel/ko_wt_down_cp_esmer.xlsx before writing the tables.

## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 10, TcCLB.409625 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.

## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 10, TcCLB.409625 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.
## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 10, TcCLB.409625 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.
## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 10, TcCLB.409625 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.

## ! # 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 in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : 671.90, TcCLB.50 ... TcCLB.511921.140 is truncated. 
## Number of characters exeed the limit of 32767.

## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

tc_nonesmer_down_cp <- simple_clusterprofiler(
  ko_wt_down, de_table = ko_wt_all, orgdb = nonesmer_db, orgdb_to = "GID",
  organism = "tcruzi", excel = "excel/ko_wt_down_cp_nonesmer.xlsx")

## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## 
## preparing geneSet collections...
## GSEA analysis...

## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (30.08% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 36
## pathways for which P-values were not calculated properly due to unbalanced (positive and negative)
## gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the
## pathways the P-values were likely overestimated. For such pathways log2err is set to NA.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in
## reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.

## leading edge analysis...
## done...
## Deleting the file excel/ko_wt_down_cp_nonesmer.xlsx before writing the tables.
## ! # 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.
## ! # 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.
## ! # 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 in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, :  TcCLB.409625.10 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.

## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, :  TcCLB.409625.10 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.
## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, :  TcCLB.409625.10 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.
## Warning in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, :  TcCLB.409625.10 ...  TcCLB.511923.60 is truncated. 
## Number of characters exeed the limit of 32767.

## ! # 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 in wb$writeData(df = x, colNames = TRUE, sheet = sheet, startRow = startRow, : CLB.506965.20, T ... TcCLB.511921.140 is truncated. 
## Number of characters exeed the limit of 32767.

## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

tc_unas_down_cp <- simple_clusterprofiler(
  ko_wt_down, de_table = ko_wt_all, orgdb = unas_db, orgdb_to = "GID",
  organism = "tcruzi")

## --> No gene can be mapped....
## --> Expected input gene ID: TcCLB.505051.20,TcCLB.507153.10,TcCLB.506189.10,TcCLB.509961.90,TcCLB.470399.10,TcCLB.507697.10
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## 
## preparing geneSet collections...
## GSEA analysis...

## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (67.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 20
## pathways for which P-values were not calculated properly due to unbalanced (positive and negative)
## gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the
## pathways the P-values were likely overestimated. For such pathways log2err is set to NA.

## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in
## reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.

## leading edge analysis...
## done...

tc_esmer_down_cp

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)

## Found 29 go_db genes and 48 length_db genes out of 48.
## Testing that go categories are defined.
## Removing undefined categories.
## Gathering synonyms.
## Gathering category definitions.

mf_enr <- tc_up_gs[["mf_enrich"]]
mf_plots <- plot_enrichresult(mf_enr)

## Warning in fortify(object, showCategory = showCategory, by = x, ...): Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = x
## ℹ Did you misspell an argument name?

## Warning in fortify(data, ...): Arguments in `...` must be used.
## ✖ Problematic arguments:
## • as.Date = as.Date
## • yscale_mapping = yscale_mapping
## • show.legend = FALSE
## ℹ Did you misspell an argument name?

## ! # 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.

mf_plots[["tree"]]

bp_enr <- tc_up_gs[["bp_enrich"]]
bp_plots <- plot_enrichresult(bp_enr)

## Warning in fortify(object, showCategory = showCategory, by = x, ...): Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = x
## ℹ Did you misspell an argument name?

bp_plots[["dot"]]

Now check the position of the expected lower expression genes in the context of all genes compared to wt.

message("Pull the ko_wt_all table and see where expected_lower compares.")

## Pull the ko_wt_all table and see where expected_lower compares.

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/Documents/bibtex/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)
```

# TODO

## 202512

1.  Define a set of consistent colors.  I think have darker shades for
    the human, but the same colors for both.
2.  Define a dataset which includes our previous CL-Brener/CL-14 data.
3.  We should receive some metadata including infection numbers
    (particularly for experiment #3), make use of this.
4.  Define a consistent naming scheme. (condition_batch perhaps)
5.  Define some expected numbers of expressed genes for different
    human/mammalian cell types.  This experiment is HeLa, but I think
    it would be a nice bit of context to explicitly see how it
    compares to other organisms/cell types.
6.  Add an outlier gene labeler for boxplots and/or print a table of
    outliers in plot_boxplot().
7.  Figure out some good metrics to see if the number of not-observed
    genes is relevant to the other results. (plot_prepost is one
    possibility)
8.  Plot coefficient of variance vs. batch/condition/etc.
9.  Run variance partition
10. Note: 16 specific genes were knocked out via the addition of PTCs,
    make use of our freebayes/etc tools to find/quantify them.
11. Once we have the combined experiment, check batch #3 for how it
    looks with respect to other timepoints.
12. Make sure the Cas samples are gone after early plot(s).
13. Perform DE with BiM/sva/combat, compare the results.
14. Check/clean multigene families.
15. Consider with/out batch #3
16. On the way to that, perform comparisons of batch 3 vs. batch 1/2;
    perhaps the results will tell us about the batch.
17. Use kraken to see if there are reads which explain the difference
    between batch 3 and 1/2.  E.g. is there any contamination?  We can
    mostly assume there is not because of the change in human reads.
18. To that end, provide an explicit ratio of reads/readsmapped/etc
    for hs/tc or tc/hs

# 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...

# Trans-sialidase genes which were modified

We received an email flagging the following genes as CRISPR/Cas9
targets for the knockouts.  I therefore would like to have screenshots
of each of these regions to show what differences are observable
between the three strains.  Note that the lower coverage of the last
few samples may mean that we need to stick to the first group.

* TcCLB.508173.120   Has putative GPI signal
* TcCLB.509495.30    Has putative GPI and SAPA repeat
* TcCLB.510055.20    GPI
* TcCLB.506961.25    GPI 'repeats but might not be sapa'
* TcCLB.510787.10    GPI 'sapa repeats'
* TcCLB.511667.30    gpi
* TcCLB.507085.30    gpi, highlighted green tyrosine, 'sapa repeats'
* TcCLB.507427.10    gpi
* TcCLB.508913.25    gpi
* TcCLB.508857.30    gpi
* TcCLB.503993.10    gpi
* TcCLB.511323.10    gpi 'sapa repeats'
* TcCLB.508089.10    gpi
* TcCLB.508717.60    gpi
* TcCLB.506975.80    gpi
* TcCLB.505931.30    gpi
* TcCLB.507979.30    gpi
* TcCLB.509817.50    gpi
* TcCLB.506841.20    gpi

```{r}
expected_lower <- c("TcCLB.508173.120", "TcCLB.509495.30", "TcCLB.510055.20", "TcCLB.506961.25",
                    "TcCLB.510787.10", "TcCLB.511667.30", "TcCLB.507085.30", "TcCLB.507427.10",
                    "TcCLB.508913.25", "TcCLB.508857.30", "TcCLB.503993.10", "TcCLB.511323.10",
                    "TcCLB.508089.10", "TcCLB.508717.60", "TcCLB.506975.80", "TcCLB.505931.30",
                    "TcCLB.507979.30", "TcCLB.509817.50", "TcCLB.506841.20")
```

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

## 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)
```

# Define colors

```{r}
color_choices <- list(
  "hs" = list(
    "AB10" = "#086448",
    "cas" = "#702601",
    "control" = "#454178",
    "ko7" = "#870649",
    "positive" = "#46060E",
    "wt" = "#785C01"),
  "tc" = list(
    "AB10" = "#0DA877",
    "cas" = "#BA3F01",
    "control" = "#7771D1",
    "ko7" = "#BF086A",
    "positive" = "#8F0C1E",
    "wt" = "#AF8401"))
```

These colors are bad, the human are too dark and lose their contrast
with respect to each other.  I should get Najib/April/Amalie to help
define better.

# 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") %>%
  set_colors(color_choices[["hs"]])

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") %>%
  set_colors(color_choices[["tc"]])
```

# A Few initial plots

I picked off another TODO in here, I changed plot_nonzero to more
intelligently set the text annotation.

```{r}
plot_nonzero(tc_se)
tc_se <- subset_se(tc_se, nonzero = 5000)
plot_nonzero(tc_se, y_intercept = 0.9)

plot_libsize(hs_se)
plot_nonzero(hs_se)
plot_boxplot(hs_se)

plot_libsize(tc_se)
plot_nonzero(tc_se, y_intercept = 0.9)
plot_boxplot(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)
```

# Variance Partition

Here are a couple of variance partition invocations.  Once we have
other metadata, this will be more useful.  Note, this will only work
once the non-replicated conditions are removed (control and cas).

```{r}
hs_varpart <- simple_varpart(hs_replicated)
hs_varpart
hs_varpart[["percent_plot"]]

tc_varpart <- simple_varpart(tc_replicated)
tc_varpart
tc_varpart[["percent_plot"]]
```

I think we probably should not be surprised at the amount of variance
attributed to the batch due to the very large difference in coverage
between experiment #3 and 1/2.

# Sample clustering

## Human

Perform our default PCA plot along with a combat version.

```{r}
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_nb <- normalize(hs_replicated, transform = "log2", convert = "cpm",
                   filter = TRUE, batch = "svaseq")
plot_pca(hs_nb)

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

## Parasite

```{r}
tc_norm <- normalize(tc_se, transform = "log2", convert = "cpm",
                     norm = "quant", filter = TRUE)
plot_disheat(tc_norm)
plot_corheat(tc_norm)
norm_boxplot <- plot_boxplot(tc_norm)
norm_boxplot[["plot"]]
high_genes <- rownames(norm_boxplot[["high_outlier_df"]])
fData(tc_se)[high_genes, ][["annot_transcript_product"]]
plot_pca(tc_norm, plot_labels = TRUE)

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

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

# Differential Expression

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
```

While it is true there are not a tremendous number of genes, at least
some of the groups are interesting.

```{r}
hs_gp <- all_gprofiler(hs_sig)
hs_gp
plot_enrichresult(hs_gp[["ko_vs_control_up"]][["BP_enrich"]])
plot_enrichresult(hs_gp[["ko_vs_control_down"]][["BP_enrich"]])
```

I think it is noteworthy that there is a difference in T cell proliferation.


```{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

We cannot use gProfiler2 with the parasite because it is not a
reference species; but other ontology methods are not constrained
thus.  In the case of clusterProfiler, there is another constraint, I
do not have a single orgDB object which comprises
Esmer/NonEsmer/Unassigned; as a result I must attempt the ontology
search on the haplotypes separately.

```{r}
ko_wt_up <- tc_sig[["deseq"]][["ups"]][["ko_vs_wt"]]
ko_wt_down <- tc_sig[["deseq"]][["downs"]][["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", excel = "excel/ko_wt_up_cp_esmer.xlsx")
tc_nonesmer_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = nonesmer_db, orgdb_to = "GID",
  organism = "tcruzi", excel = "excel/ko_wt_up_cp_nonesmer.xlsx")
tc_unas_up_cp <- simple_clusterprofiler(
  ko_wt_up, de_table = ko_wt_all, orgdb = unas_db, orgdb_to = "GID",
  organism = "tcruzi")
tc_esmer_up_cp


tc_esmer_down_cp <- simple_clusterprofiler(
  ko_wt_down, de_table = ko_wt_all, orgdb = esmer_db, orgdb_to = "GID",
  organism = "tcruzi", excel = "excel/ko_wt_down_cp_esmer.xlsx")
tc_nonesmer_down_cp <- simple_clusterprofiler(
  ko_wt_down, de_table = ko_wt_all, orgdb = nonesmer_db, orgdb_to = "GID",
  organism = "tcruzi", excel = "excel/ko_wt_down_cp_nonesmer.xlsx")
tc_unas_down_cp <- simple_clusterprofiler(
  ko_wt_down, de_table = ko_wt_all, orgdb = unas_db, orgdb_to = "GID",
  organism = "tcruzi")
tc_esmer_down_cp

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"]]
```

Now check the position of the expected lower expression genes in the
context of all genes compared to wt.

```{r}
message("Pull the ko_wt_all table and see where expected_lower compares.")
```


```{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)
```


Examining some cruzi infected HeLa cells.

atb abelew@gmail.com

2026-01-09