Testing parallel processing of pathway enrichment analysis
2024-07-22
Source: TBA
Intro
How many parallel threads should be used for pathway enrichment analysis?
AMD Ryzen Threadripper 1900X 8-Core Processor (16 parallel threads).
BiocManager::install(c("getDEE2","DESeq2","mitch","fgsea"))## 'getOption("repos")' replaces Bioconductor standard repositories, see
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## CRAN: https://packagemanager.rstudio.com/all/__linux__/focal/latest## Bioconductor version 3.13 (BiocManager 1.30.16), R 4.1.1 (2021-08-10)## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
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## 'survival'install.packages(c("tictoc","RhpcBLASctl","peakRAM"))## Installing packages into '/usr/local/lib/R/site-library'
## (as 'lib' is unspecified)library("getDEE2")
library("DESeq2")## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
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## anyMissing, rowMedianslibrary("mitch")## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library("fgsea")
library("tictoc")##
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## shiftlibrary("RhpcBLASctl")
library("peakRAM")
blas_set_num_threads(1)Get gene expression data
download.file("https://ziemann-lab.net/public/fgseatest/de.Rds",
"de.Rds")
de <- readRDS("de.Rds")
head(de)## baseMean log2FoldChange lfcSE stat
## ENSG00000165949 IFI27 1960.1970 -3.384492 0.09388689 -36.04861
## ENSG00000090382 LYZ 7596.0299 -1.650342 0.05611430 -29.41036
## ENSG00000115461 IGFBP5 531.2217 -5.071157 0.17952391 -28.24781
## ENSG00000157601 MX1 827.1511 -2.877795 0.10478234 -27.46450
## ENSG00000111331 OAS3 2127.2010 -2.661214 0.09721242 -27.37525
## ENSG00000070915 SLC12A3 424.5509 -3.374852 0.12986708 -25.98697
## pvalue padj SRR1171523 SRR1171524
## ENSG00000165949 IFI27 1.450013e-284 1.909377e-280 12.05759 12.12946
## ENSG00000090382 LYZ 4.048160e-190 2.665308e-186 13.52939 13.52615
## ENSG00000115461 IGFBP5 1.514307e-175 6.646797e-172 10.60714 10.46316
## ENSG00000157601 MX1 4.663288e-166 1.535154e-162 10.88831 11.08737
## ENSG00000111331 OAS3 5.406541e-165 1.423867e-161 11.92053 12.26289
## ENSG00000070915 SLC12A3 6.951548e-149 1.525633e-145 10.35061 10.33824
## SRR1171525 SRR1171526 SRR1171527 SRR1171528
## ENSG00000165949 IFI27 11.82385 9.646471 9.705799 9.623453
## ENSG00000090382 LYZ 13.62313 12.080100 12.012891 12.031277
## ENSG00000115461 IGFBP5 10.69892 8.568916 8.566744 8.693134
## ENSG00000157601 MX1 10.86873 9.322793 9.356473 9.267699
## ENSG00000111331 OAS3 11.91655 10.108651 10.070989 10.012229
## ENSG00000070915 SLC12A3 10.26395 8.844934 8.904787 8.871748Get pathways
download.file("https://ziemann-lab.net/public/fgseatest/c5.go.v2023.2.Hs.symbols.gmt",
"c5.go.v2023.2.Hs.symbols.gmt")
pw <- gmt_import("c5.go.v2023.2.Hs.symbols.gmt")Mitch
gt <- data.frame(rownames(de))
gt$g <- sapply(strsplit(gt[,1]," "),"[[",2)
m <- mitch_import(x=de,DEtype="deseq2",geneTable=gt)## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 13168## Note: no. genes in output = 13164## Note: estimated proportion of input genes in output = 1corerange <- 1:16
mres <- lapply(corerange, function(cores) {
tic()
mres <- mitch_calc(x=m,genesets=pw,cores=cores)
toc()
} )## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 46.789 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 27.022 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 22.123 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 19.781 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 18.546 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 14.706 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 16.227 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 15.224 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 15.191 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 14.105 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 15.127 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 16.851 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 14.302 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 14.866 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 15.508 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 16.329 sec elapsedpeakRAM(mxres <- mitch_calc(x=m,genesets=pw,cores=1))## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## Function_Call Elapsed_Time_sec
## 1 mxres<-mitch_calc(x=m,genesets=pw,cores=1) 43.48
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 1 122.7mres <- do.call(rbind,lapply(mres,unlist))
mres <- as.numeric(mres[,2]) - as.numeric(mres[,1])
names(mres) <- corerange
mres## 1 2 3 4 5 6 7 8 9 10 11
## 46.789 27.022 22.123 19.781 18.546 14.706 16.227 15.224 15.191 14.105 15.127
## 12 13 14 15 16
## 16.851 14.302 14.866 15.508 16.329barplot(mres,ylab="elapsed time in s",xlab="parallel threads", main="mitch")
FGSEA
f <- as.vector(m[,1])
names(f) <- rownames(m)
corerange <- 1:16
fres <- lapply(corerange, function(cores) {
tic()
fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=cores)
toc()
} )## Warning in fgseaMultilevel(...): There were 29 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 99.256 sec elapsed## Warning in fgseaMultilevel(...): There were 24 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 52.141 sec elapsed## Warning in fgseaMultilevel(...): There were 21 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 41.858 sec elapsed## Warning in fgseaMultilevel(...): There were 14 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 33.207 sec elapsed## Warning in fgseaMultilevel(...): There were 23 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 26.326 sec elapsed## Warning in fgseaMultilevel(...): There were 21 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 22.537 sec elapsed## Warning in fgseaMultilevel(...): There were 19 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 22.489 sec elapsed## Warning in fgseaMultilevel(...): There were 27 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 19.19 sec elapsed## Warning in fgseaMultilevel(...): There were 35 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(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 21.085 sec elapsed## Warning in fgseaMultilevel(...): There were 30 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 18.788 sec elapsed## Warning in fgseaMultilevel(...): 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 18.803 sec elapsed## Warning in fgseaMultilevel(...): There were 26 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 17.278 sec elapsed## Warning in fgseaMultilevel(...): 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 17.683 sec elapsed## Warning in fgseaMultilevel(...): 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 16.674 sec elapsed## Warning in fgseaMultilevel(...): There were 12 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 19.324 sec elapsed## Warning in fgseaMultilevel(...): 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## 17.176 sec elapsedblas_set_num_threads(1)
peakRAM(fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=1))## Warning in fgseaMultilevel(...): There were 21 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## Function_Call Elapsed_Time_sec
## 1 fgseaRes<-fgsea(pathways=pw,stats=f,minSize=10,nproc=1) 101.992
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 -23.2 87.4blas_set_num_threads(8)
peakRAM(fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=1))## Warning in fgseaMultilevel(...): There were 10 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## Function_Call Elapsed_Time_sec
## 1 fgseaRes<-fgsea(pathways=pw,stats=f,minSize=10,nproc=1) 107.143
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 8.3 118.8blas_set_num_threads(1)
peakRAM(fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=8))## Warning in fgseaMultilevel(...): There were 26 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## Function_Call Elapsed_Time_sec
## 1 fgseaRes<-fgsea(pathways=pw,stats=f,minSize=10,nproc=8) 16.661
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 9.1 118.8blas_set_num_threads(4)
peakRAM(fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=4))## Warning in fgseaMultilevel(...): There were 24 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(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are
## less than 1e-10. You can set the `eps` argument to zero for better estimation.## Function_Call Elapsed_Time_sec
## 1 fgseaRes<-fgsea(pathways=pw,stats=f,minSize=10,nproc=4) 29.379
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 9.1 118.8fres <- do.call(rbind,lapply(fres,unlist))
fres <- as.numeric(fres[,2]) - as.numeric(fres[,1])
names(fres) <- corerange
fres## 1 2 3 4 5 6 7 8 9 10 11
## 99.256 52.141 41.858 33.207 26.326 22.537 22.489 19.190 21.085 18.788 18.803
## 12 13 14 15 16
## 17.278 17.683 16.674 19.324 17.176barplot(fres,ylab="elapsed time in s",xlab="parallel threads", main="fgsea")
Session information
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] peakRAM_1.0.2 RhpcBLASctl_0.23-42
## [3] tictoc_1.2.1 fgsea_1.18.0
## [5] mitch_1.4.1 DESeq2_1.32.0
## [7] SummarizedExperiment_1.22.0 Biobase_2.52.0
## [9] MatrixGenerics_1.4.3 matrixStats_1.3.0
## [11] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
## [13] IRanges_2.26.0 S4Vectors_0.30.2
## [15] BiocGenerics_0.38.0 getDEE2_1.2.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5 RColorBrewer_1.1-3
## [4] httr_1.4.2 tools_4.1.1 bslib_0.7.0
## [7] utf8_1.2.2 R6_2.5.1 KernSmooth_2.23-20
## [10] DBI_1.2.3 colorspace_2.1-0 htm2txt_2.2.2
## [13] tidyselect_1.2.1 gridExtra_2.3 GGally_2.2.1
## [16] bit_4.0.5 compiler_4.1.1 cli_3.6.3
## [19] DelayedArray_0.18.0 sass_0.4.9 caTools_1.18.2
## [22] scales_1.3.0 genefilter_1.74.1 stringr_1.5.1
## [25] digest_0.6.28 rmarkdown_2.27 XVector_0.32.0
## [28] pkgconfig_2.0.3 htmltools_0.5.8.1 highr_0.11
## [31] fastmap_1.2.0 htmlwidgets_1.6.4 rlang_1.1.4
## [34] RSQLite_2.3.7 shiny_1.8.1.1 jquerylib_0.1.4
## [37] generics_0.1.0 jsonlite_1.7.2 gtools_3.9.5
## [40] BiocParallel_1.26.2 dplyr_1.1.4 RCurl_1.98-1.16
## [43] magrittr_2.0.1 GenomeInfoDbData_1.2.6 Matrix_1.3-4
## [46] Rcpp_1.0.13 munsell_0.5.1 fansi_0.5.0
## [49] lifecycle_1.0.4 stringi_1.7.4 yaml_2.2.1
## [52] MASS_7.3-54 zlibbioc_1.38.0 gplots_3.1.3.1
## [55] plyr_1.8.9 grid_4.1.1 ggstats_0.6.0
## [58] blob_1.2.4 promises_1.3.0 crayon_1.4.1
## [61] lattice_0.20-45 Biostrings_2.60.2 echarts4r_0.4.5
## [64] splines_4.1.1 annotate_1.70.0 KEGGREST_1.32.0
## [67] locfit_1.5-9.10 knitr_1.48 pillar_1.9.0
## [70] reshape2_1.4.4 geneplotter_1.70.0 fastmatch_1.1-4
## [73] XML_3.99-0.17 glue_1.7.0 evaluate_0.24.0
## [76] data.table_1.15.4 BiocManager_1.30.16 png_0.1-8
## [79] vctrs_0.6.5 httpuv_1.6.15 gtable_0.3.5
## [82] purrr_1.0.2 tidyr_1.3.1 cachem_1.0.6
## [85] ggplot2_3.5.1 xfun_0.46 mime_0.11
## [88] xtable_1.8-4 later_1.3.2 survival_3.2-13
## [91] tibble_3.2.1 beeswarm_0.4.0 AnnotationDbi_1.54.1
## [94] memoise_2.0.1