202405: Changed excel output directory to match organization scheme in box. Generally this means files go to analyses/transcriptome/{type_of_contrast}/{date}/something_{suffix}.xlsx Where suffix is _table for the full tables and _sig for the significant genes and will include information about whether sva etc was used. 202405: Adding some goseq results.
** Note! ** The new definitions of susceptible/resistant are tighter than ever before, as a result there are no longer any ambiguous samples. Thus I removed the ambiguous contrasts in the following block.
Just a reminder that in data_structures.Rmd I created lp_go and lp_lengths
Najib read me an email listing off the gene names associated with the zymodeme classification. I took those names and cross referenced them against the Leishmania panamensis gene annotations and found the following:
They are:
Given these 6 gene IDs (NH has two gene IDs associated with it), I can do some looking for specific differences among the various samples.
The following creates a colorspace (red to green) heatmap showing the observed expression of these genes in every sample.
my_genes <- c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
"LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300",
"other")
my_names <- c("ALAT", "ASAT", "G6PD", "NHv1", "NHv2", "MPI", "other")
zymo_six_genes <- exclude_genes_expt(lp_two_strains, ids = my_genes, method = "keep")## Error in exclude_genes_expt(lp_two_strains, ids = my_genes, method = "keep"): could not find function "exclude_genes_expt"strain_norm <- normalize_expt(zymo_six_genes, convert = "rpkm", filter = TRUE, transform = "log2",
column = "annot_cds_length")## Error in h(simpleError(msg, call)): error in evaluating the argument 'input' in selecting a method for function 'state': object 'zymo_six_genes' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_sample_heatmap': object 'strain_norm' not found## Error: object 'zymo_heatmap' not foundlp_norm <- normalize_expt(lp_two_strains, filter = TRUE, convert = "cpm",
norm = "quant", transform = "log2", column = "annot_cds_length")## Error in h(simpleError(msg, call)): error in evaluating the argument 'input' in selecting a method for function 'state': object 'lp_two_strains' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_sample_heatmap': object 'lp_norm' not found## Error: object 'zymo_heatmap_all' not foundI want to compare the above heatmap with one which is comprised of all genes with some ‘significantly high’ expression value and also a not-negligible coefficient of variance.
## Error in h(simpleError(msg, call)): error in evaluating the argument 'input' in selecting a method for function 'state': object 'lp_two_strains' not foundhigh_strain_norm <- normalize_expt(zymo_high_genes, convert = "rpkm",
norm = "quant", transform = "log2", column = "annot_cds_length")## Error in h(simpleError(msg, call)): error in evaluating the argument 'input' in selecting a method for function 'state': object 'zymo_high_genes' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'data' in selecting a method for function 'plot_sample_heatmap': object 'high_strain_norm' not found## Error: object 'zymo_heatmap' not foundI think this plot suggests that the difference between the two primary strains is not really one of a few specific genes, but instead a global pattern.
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_zymo' not found## Error: object 'zymo_de_nobatch' not found## Including the plots causes the rda file to balloon to 3.4Gb in the following invocation.
## Removing them results in... holy crap 2.1Mb
zymo_table_nobatch <- combine_de_tables(
zymo_de_nobatch, keepers = zymodeme_keeper, label_column = "annot_gene_product",
rda = glue("rda/zymo_tables_nobatch-v{ver}.rda"),
excel = glue("{excel_out}/DE_Strain/{ver}/zymo_tables_nobatch-v{ver}.xlsx"))## Error: object 'zymo_de_nobatch' not found## Error: object 'zymo_table_nobatch' not foundzymo_sig_nobatch <- extract_significant_genes(
zymo_table_nobatch,
according_to = "deseq", current_id = "GID", required_id = "GID",
gmt = glue("excel/zymodeme_nobatch-v{ver}.gmt"),
excel = glue("{excel_out}/DE_Strain/{ver}/zymo_sig_nobatch_deseq-v{ver}.xlsx"))## Error: object 'zymo_table_nobatch' not found## Error: object 'zymo_sig_nobatch' not foundThere are too few genes at our current stringencies for a meaningful result.
## Error: object 'zymo_sig_nobatch' not found## Error: object 'zymo_sig_nobatch' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'increased_z22' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'increased_z23' not found## Error: object 'zymo_table_nobatch' not found## Error: object 'zymo_table_nobatch' not foundLog ratio, mean average plot and volcano plot of the comparison of the two primary zymodeme transcriptomes. When the transcriptomes of the two main strains (43 and 41 samples of z2.3 and z2.1) were compared without any attempt at batch/surrogate estimation with DESeq2, 45 and 85 genes were observed as significantly higher in strain z2.3 and z2.2 respectively using a cutoff of 1.0 logFC and 0.05 FDR adjusted p-value. There remain a large number of genes which are likely significantly different between the two strains, but fall below the 2-fold difference required for ‘significance.’ This follows prior observations that the parasite transcriptomes are constituitively expressed.
When the same data was plotted via a volcano plot, the relatively small range of fold changes compared to the large range of adjusted p-values is visible.
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_zymo' not found## Error: object 'zymo_de_sva' not foundzymo_table_sva <- combine_de_tables(
zymo_de_sva, keepers = zymodeme_keeper, label_column = "annot_gene_product",
rda = glue("rda/zymo_tables_sva-v{ver}.rda"),
excel = glue("{excel_out}/DE_Strain/{ver}/zymo_tables_sva-v{ver}.xlsx"))## Error: object 'zymo_de_sva' not found## Error: object 'zymo_table_sva' not foundzymo_sig_sva <- extract_significant_genes(
zymo_table_sva,
according_to = "deseq",
current_id = "GID", required_id = "GID",
gmt = glue("excel/zymodeme_sva-v{ver}.gmt"),
excel = glue("{excel_out}/DE_Strain/{ver}/zymo_sig_sva-v{ver}.xlsx"))## Error: object 'zymo_table_sva' not found## Error: object 'zymo_sig_sva' not foundThere are too few genes at our current stringencies for a meaningful result.
## Error: object 'zymo_sig_sva' not found## Error: object 'zymo_sig_sva' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'increased_z22' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'increased_z23' not foundWhen estimates from SVA were included in the statistical model used by EdgeR, DESeq2, and limma; a nearly identical view of the data emerged. I think this shows with a high degree of confidence, that sva is not having a significant effect on this dataset.
## Error: object 'zymo_table_sva' not found## Error: object 'zymo_table_sva' not foundThis susceptibility comparison is using the ‘current’ dataset.
Note again: we no longer have any ambiguous samples, so I commented out a portion of the following block.
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_susceptibility' not found## Error: object 'sus_de_nobatch' not foundsus_table_nobatch <- combine_de_tables(
sus_de_nobatch, keepers = susceptibility_keepers,
rda = glue("rda/sus_tables_nobatch-v{ver}.rda"),
excel = glue("{excel_out}/DE_Susceptibility/{ver}/sus_tables_nobatch-v{ver}.xlsx"))## Error: object 'sus_de_nobatch' not found## Error: object 'sus_table_nobatch' not foundsus_sig_nobatch <- extract_significant_genes(
sus_table_nobatch,
excel = glue("{excel_out}/DE_Susceptibility/{ver}/sus_sig_nobatch-v{ver}.xlsx"))## Error: object 'sus_table_nobatch' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_susceptibility' not found## Error: object 'sus_de_sva' not foundsus_table_sva <- combine_de_tables(
sus_de_sva, keepers = susceptibility_keepers,
rda = glue("rda/sus_tables_sva-v{ver}.rda"),
excel = glue("{excel_out}/DE_Susceptibility/{ver}/sus_tables_sva-v{ver}.xlsx"))## Error: object 'sus_de_sva' not found## Error: object 'sus_table_sva' not foundsus_sig_sva <- extract_significant_genes(
sus_table_sva, according_to = "deseq",
excel = glue("{excel_out}/DE_Susceptibility/{ver}/sus_sig_sva-v{ver}.xlsx"))## Error: object 'sus_table_sva' not found## Error: object 'sus_sig_sva' not found## To get a more true sense of sensitive vs resistant with sva, we kind of need to get rid of the
## unknown samples and perhaps the ambiguous.
## no_ambiguous <- subset_expt(lp_susceptibility, subset = "condition!='ambiguous'") %>%
## subset_expt(subset = "condition!='unknown'")
## no_ambiguous_de_sva <- all_pairwise(no_ambiguous, filter = TRUE, model_batch = "svaseq")
## no_ambiguous_de_sva
## Let us see if my keeper code will fail hard or soft with extra contrasts...
## no_ambiguous_table_sva <- combine_de_tables(
## no_ambiguous_de_sva, keepers = susceptibility_keepers,
## excel = glue("excel/no_ambiguous_tables_sva-v{ver}.xlsx"))
## no_ambiguous_table_sva
## no_ambiguous_sig_sva <- extract_significant_genes(
## no_ambiguous_table_sva, according_to = "deseq",
## excel = glue("excel/no_ambiguous_sig_sva-v{ver}.xlsx"))
## no_ambiguous_sig_sva## Error: object 'sus_sig_sva' not found## Error: object 'sus_sig_sva' not found## Error: object 'sus_sig_sva' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'increased_resistant' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'increased_sensitive' not found## Error: object 'sus_table_nobatch' not found## Error: object 'sus_table_nobatch' not found## Error: object 'sus_table_sva' not found## Error: object 'sus_table_sva' not foundGiven that resistance/sensitivity tends to be correlated with strain, one might expect similar results. One caveat in this context though: there are fewer strains with resistance/sensitivity definitions. This when the analysis was repeated without the ambiguous/unknown samples, a few more genes were observed as significant.
## zymo_table_sva[["plots"]][["zymodeme"]][["deseq_ma_plots"]][["plot"]]
zy_df <- zymo_table_sva[["data"]][["zymodeme"]]## Error: object 'zymo_table_sva' not found## Error: object 'sus_table_sva' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': object 'zy_df' not found## Error: object 'both_df' not found## Error: object 'both_df' not found## Error: object 'plot_df' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'plot_df' not found## Error in compare$scatter: object of type 'closure' is not subsettable## png
## 2## Error in compare$scatter: object of type 'closure' is not subsettable## Error in compare$cor: object of type 'closure' is not subsettableThis susceptibility comparison is using the historical dataset.
sushist_de_nobatch <- all_pairwise(lp_susceptibility_historical, filter = TRUE,
model_batch = FALSE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_susceptibility_historical' not found## Error: object 'sushist_de_nobatch' not foundsushist_table_nobatch <- combine_de_tables(
sushist_de_nobatch, keepers = susceptibility_keepers,
excel = glue("{excel_out}/DE_Susceptibility/sushist_tables_nobatch-v{ver}.xlsx"))## Error: object 'sushist_de_nobatch' not found## Error: object 'sushist_table_nobatch' not foundsushist_sig_nobatch <- extract_significant_genes(
sushist_table_nobatch,
excel = glue("{excel_out}/DE_Susceptibility/sushist_sig_nobatch-v{ver}.xlsx"))## Error: object 'sushist_table_nobatch' not found## Error: object 'sushist_sig_nobatch' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_susceptibility_historical' not found## Error: object 'sushist_de_sva' not foundsushist_table_sva <- combine_de_tables(
sushist_de_sva, keepers = susceptibility_keepers,
excel = glue("{excel_out}/DE_Susceptibility/sushist_tables_sva-v{ver}.xlsx"))## Error: object 'sushist_de_sva' not found## Error: object 'sushist_table_sva' not foundsushist_sig_sva <- extract_significant_genes(
sushist_table_sva, according_to = "deseq",
excel = glue("{excel_out}/DE_Susceptibility/sushist_sig_sva-v{ver}.xlsx"))## Error: object 'sushist_table_sva' not found## Error: object 'sushist_sig_sva' not found##cf_nb_input <- subset_expt(cf_expt, subset="condition!='unknown'")
cf_de_nobatch <- all_pairwise(lp_cf_known, filter = TRUE, model_batch = FALSE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_cf_known' not found## Error: object 'cf_de_nobatch' not foundcf_table_nobatch <- combine_de_tables(
cf_de_nobatch,
excel = glue("{excel_out}/DE_Cure_vs_Fail/{ver}/cf_tables_nobatch-v{ver}.xlsx"))## Error: object 'cf_de_nobatch' not found## Error: object 'cf_table_nobatch' not foundcf_sig_nobatch <- extract_significant_genes(
cf_table_nobatch,
excel = glue("{excel_out}/DE_Cure_vs_Fail/{ver}/cf_sig_nobatch-v{ver}.xlsx"))## Error: object 'cf_table_nobatch' not found## Error: object 'cf_sig_nobatch' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_cf_known' not found## Error: object 'cf_de' not foundcf_table <- combine_de_tables(
cf_de,
excel = glue("{excel_out}/DE_Cure_vs_Fail/{ver}/cf_tables-v{ver}.xlsx"))## Error: object 'cf_de' not found## Error: object 'cf_table' not foundcf_sig <- extract_significant_genes(
cf_table,
excel = glue("{excel_out}/DE_Cure_vs_Fail/{ver}/cf_sig-v{ver}.xlsx"))## Error: object 'cf_table' not found## Error: object 'cf_sig' not foundI am not going to mess with GO searches for this.
It is not surprising that few or no genes are deemed significantly differentially expressed across samples which were taken from cure or fail patients.
## Error: object 'cf_table_nobatch' not found## Error: object 'cf_table' not found## Error: object 'cf_table' not foundOne query we have not yet addressed: what are the similarities and differences among the strains used to infect the macrophage samples and the promastigote samples used in the TMRC2 parasite data?
In my container image, this dataset is not currently loaded, so turning this off.
## I just fixed this in the datasets Rmd, but until that propagates just set it manually
annotation(lp_expt) <- annotation(lp_macrophage)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'annotation': object 'lp_macrophage' not foundtmrc2_macrophage_norm <- normalize_expt(lp_macrophage, transform="log2", convert="cpm",
norm="quant", filter=TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'input' in selecting a method for function 'state': object 'lp_macrophage' not found## Error: object 'lp_expt' not found## Error: object 'all_tmrc2' not found## Error: object 'all_nosb' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'all_nosb' not found## Error: object 'all_nosb' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': object 'all_nosb' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'all_nosb' not found## Error: object 'all_nosb' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'all_nosb' not foundI think the above picture is sort of the opposite of what we want to compare in a DE analysis for this set of data, e.g. we want to compare promastigotes from amastigotes?
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': error in evaluating the argument 'expt' in selecting a method for function 'set_expt_batches': object 'all_nosb' not foundtwo_zymo <- subset_expt(
all_nosb,
subset = "zymodemecategorical=='z22'|zymodemecategorical=='z23'|zymodemecategorical=='unknown'")## Error in h(simpleError(msg, call)): error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': object 'all_nosb' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'all_nosb' not foundpro_ama_table <- combine_de_tables(
pro_ama,
excel = glue("{excel_out}/DE_promastigote_amastigote/{ver}/pro_vs_ama_table-v{ver}.xlsx"))## Error: object 'pro_ama' not foundpro_ama_sig <- extract_significant_genes(
pro_ama_table,
excel = glue("{excel_out}/DE_promastigote_amastigote/{ver}/pro_vs_ama_sig-v{ver}.xlsx"))## Error: object 'pro_ama_table' not found## Error: object 'pro_ama_sig' not found## Error: object 'pro_ama_sig' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'increased_promastigote' not found## Error: object 'promastigote_goseq' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'increased_amastigote' not found## Error: object 'amastigote_goseq' not found## silly, topgo wants the gene id column to be 'ID', I should fix this.
colnames(lp_go) <- c("ID", "GO")## Error: object 'lp_go' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'sig_genes' in selecting a method for function 'simple_topgo': object 'increased_promastigote' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'dotplot': object 'promastigote_topgo' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'sig_genes' in selecting a method for function 'simple_topgo': object 'increased_amastigote' not found## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'dotplot': object 'amastigote_topgo' not found## Error: object 'pro_ama_table' not foundI am a little surprised by this plot, I somewhat expected there to be few genes which passed the 2-fold difference demarcation line.
## Warning: Your system is mis-configured: '/etc/localtime' is not a symlink## Warning: It is strongly recommended to set envionment variable TZ to
## 'America/New_York' (or equivalent)R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
locale: C
attached base packages: stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: hpgltools(v.1.2), testthat(v.3.2.3), glue(v.1.8.0) and Heatplus(v.3.16.0)
loaded via a namespace (and not attached): fs(v.1.6.6), matrixStats(v.1.5.0), bitops(v.1.0-9), enrichplot(v.1.28.4), devtools(v.2.4.6), httr(v.1.4.7), RColorBrewer(v.1.1-3), numDeriv(v.2016.8-1.1), tools(v.4.5.0), backports(v.1.5.0), R6(v.2.6.1), lazyeval(v.0.2.2), mgcv(v.1.9-3), cli(v.3.6.5), Biobase(v.2.68.0), sass(v.0.4.10), mvtnorm(v.1.3-3), S7(v.0.2.0), genefilter(v.1.90.0), Rsamtools(v.2.24.0), systemfonts(v.1.3.1), yulab.utils(v.0.2.1), gson(v.0.1.0), DOSE(v.4.2.0), R.utils(v.2.13.0), dichromat(v.2.0-0.1), sessioninfo(v.1.2.3), limma(v.3.64.3), rstudioapi(v.0.17.1), RSQLite(v.2.4.3), generics(v.0.1.4), gridGraphics(v.0.5-1), BiocIO(v.1.18.0), gtools(v.3.9.5), dplyr(v.1.1.4), zip(v.2.3.3), GO.db(v.3.21.0), Matrix(v.1.7-3), S4Vectors(v.0.46.0), abind(v.1.4-8), R.methodsS3(v.1.8.2), lifecycle(v.1.0.4), yaml(v.2.3.10), edgeR(v.4.6.3), SummarizedExperiment(v.1.38.1), gplots(v.3.2.0), qvalue(v.2.40.0), SparseArray(v.1.8.1), grid(v.4.5.0), blob(v.1.2.4), promises(v.1.3.3), crayon(v.1.5.3), ggtangle(v.0.0.7), lattice(v.0.22-7), cowplot(v.1.2.0), GenomicFeatures(v.1.60.0), annotate(v.1.86.1), KEGGREST(v.1.48.1), pillar(v.1.11.0), knitr(v.1.50), varhandle(v.2.0.6), fgsea(v.1.34.2), GenomicRanges(v.1.60.0), rjson(v.0.2.23), boot(v.1.3-31), corpcor(v.1.6.10), codetools(v.0.2-20), fastmatch(v.1.1-6), ggiraph(v.0.9.2), ggfun(v.0.2.0), fontLiberation(v.0.1.0), data.table(v.1.17.8), remotes(v.2.5.0), vctrs(v.0.6.5), png(v.0.1-8), treeio(v.1.32.0), Rdpack(v.2.6.4), gtable(v.0.3.6), cachem(v.1.1.0), xfun(v.0.53), openxlsx(v.4.2.8), rbibutils(v.2.3), S4Arrays(v.1.8.1), mime(v.0.13), reformulas(v.0.4.1), survival(v.3.8-3), iterators(v.1.0.14), statmod(v.1.5.0), ellipsis(v.0.3.2), nlme(v.3.1-168), pbkrtest(v.0.5.5), ggtree(v.3.99.0), usethis(v.3.2.1), bit64(v.4.6.0-1), fontquiver(v.0.2.1), EnvStats(v.3.1.0), GenomeInfoDb(v.1.44.2), rprojroot(v.2.1.1), bslib(v.0.9.0), KernSmooth(v.2.23-26), BiocGenerics(v.0.54.0), DBI(v.1.2.3), DESeq2(v.1.48.1), tidyselect(v.1.2.1), bit(v.4.6.0), compiler(v.4.5.0), curl(v.7.0.0), graph(v.1.86.0), desc(v.1.4.3), fontBitstreamVera(v.0.1.1), DelayedArray(v.0.34.1), plotly(v.4.11.0), rtracklayer(v.1.68.0), scales(v.1.4.0), caTools(v.1.18.3), remaCor(v.0.0.20), rappdirs(v.0.3.3), stringr(v.1.5.1), digest(v.0.6.37), minqa(v.1.2.8), variancePartition(v.1.38.1), rmarkdown(v.2.29), aod(v.1.3.3), XVector(v.0.48.0), RhpcBLASctl(v.0.23-42), htmltools(v.0.5.8.1), pkgconfig(v.2.0.3), lme4(v.1.1-37), MatrixGenerics(v.1.20.0), fastmap(v.1.2.0), rlang(v.1.1.6), htmlwidgets(v.1.6.4), UCSC.utils(v.1.4.0), shiny(v.1.11.1), farver(v.2.1.2), jquerylib(v.0.1.4), jsonlite(v.2.0.0), BiocParallel(v.1.42.1), GOSemSim(v.2.34.0), R.oo(v.1.27.1), RCurl(v.1.98-1.17), magrittr(v.2.0.4), GenomeInfoDbData(v.1.2.14), ggplotify(v.0.1.2), patchwork(v.1.3.2), Rcpp(v.1.1.0), ape(v.5.8-1), gdtools(v.0.4.4), stringi(v.1.8.7), brio(v.1.1.5), MASS(v.7.3-65), plyr(v.1.8.9), pkgbuild(v.1.4.8), parallel(v.4.5.0), ggrepel(v.0.9.6), forcats(v.1.0.0), Biostrings(v.2.76.0), splines(v.4.5.0), pander(v.0.6.6), locfit(v.1.5-9.12), igraph(v.2.1.4), reshape2(v.1.4.4), stats4(v.4.5.0), pkgload(v.1.4.1), XML(v.3.99-0.19), evaluate(v.1.0.4), BiocManager(v.1.30.26), nloptr(v.2.2.1), PROPER(v.1.40.0), foreach(v.1.5.2), httpuv(v.1.6.16), tidyr(v.1.3.1), purrr(v.1.1.0), ggplot2(v.4.0.0), broom(v.1.0.10), xtable(v.1.8-4), restfulr(v.0.0.16), fANCOVA(v.0.6-1), tidytree(v.0.4.6), later(v.1.4.3), viridisLite(v.0.4.2), tibble(v.3.3.0), lmerTest(v.3.1-3), clusterProfiler(v.4.16.0), aplot(v.0.2.8), memoise(v.2.0.1), AnnotationDbi(v.1.70.0), GenomicAlignments(v.1.44.0), IRanges(v.2.42.0), sva(v.3.56.0) and GSEABase(v.1.70.0)
## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 5043179aa73cd71040a7ba82276b0cf26cc661bd## This is hpgltools commit: Mon Oct 6 12:01:25 2025 -0400: 5043179aa73cd71040a7ba82276b0cf26cc661bd