Testing parallel processing of pathway enrichment analysis
2024-07-23
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("mitch","fgsea"))## 'getOption("repos")' replaces Bioconductor standard repositories, see
## 'help("repositories", package = "BiocManager")' for details.
## Replacement repositories:
## CRAN: https://p3m.dev/cran/__linux__/jammy/latest## Bioconductor version 3.19 (BiocManager 1.30.23), R 4.4.1 (2024-06-14)## Installing package(s) 'mitch', 'fgsea'## also installing the dependencies 'bit', 'bit64', 'vroom', 'tzdb', 'readr', 'haven', 'labelled', 'formatR', 'generics', 'tidyselect', 'broom.helpers', 'forcats', 'patchwork', 'hms', 'bitops', 'backports', 'lambda.r', 'futile.options', 'colorspace', 'dplyr', 'tidyr', 'ggstats', 'gtable', 'progress', 'RColorBrewer', 'isoband', 'gtools', 'caTools', 'countrycode', 'broom', 'corrplot', 'viridisLite', 'svglite', 'futile.logger', 'snow', 'farver', 'labeling', 'munsell', 'plyr', 'reshape2', 'GGally', 'gridExtra', 'ggplot2', 'gplots', 'beeswarm', 'echarts4r', 'kableExtra', 'data.table', 'BiocParallel', 'cowplot', 'fastmatch', 'scales', 'BH'## Old packages: 'gert', 'httr2', 'knitr', 'pkgdown', 'pkgload', 'ps', 'Rcpp',
## 'roxygen2', 'tinytex', 'xfun', 'yaml', 'foreign'install.packages(c("tictoc","RhpcBLASctl","peakRAM"))## Installing packages into '/usr/local/lib/R/site-library'
## (as 'lib' is unspecified)library("mitch")## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library("fgsea")
library("tictoc")
library("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.## 32.464 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 18.132 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 13.063 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 12.147 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 11.464 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 8.527 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 9.134 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 9.419 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 8.059 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 9.473 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 8.52 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 8.062 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 8.607 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 9.386 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 7.897 sec elapsed## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 9.023 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) 29.247
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 1 110.4mres <- 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
## 32.464 18.132 13.063 12.147 11.464 8.527 9.134 9.419 8.059 9.473 8.520
## 12 13 14 15 16
## 8.062 8.607 9.386 7.897 9.023barplot(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(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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | 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|===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 120.068 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 16 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | 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|======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 69.755 sec elapsed## 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | 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|===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some pathways, in reality P-values are less than 1e-50. You can
## set the `eps` argument to zero for better estimation.## 46.476 sec elapsed## 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 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## 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-50. You can
## set the `eps` argument to zero for better estimation.## 35.12 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | 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|======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 28.838 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 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## 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-50. You can
## set the `eps` argument to zero for better estimation.## 24.75 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 31 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | 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|===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 21.222 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== 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|===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some pathways, in reality P-values are less than 1e-50. You can
## set the `eps` argument to zero for better estimation.## 20.668 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | 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|===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 17.689 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | 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|======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 17.154 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | 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|===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 14.812 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 17 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | 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|===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 15.143 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== 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|===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 14.409 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 15 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 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|===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 13.716 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | 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|===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 12.449 sec elapsed## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== 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|===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. You can
## set the `eps` argument to zero for better estimation.## 13.005 sec elapsedblas_set_num_threads(1)
peakRAM(fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=1))## 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 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## 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-50. 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) 121.843
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 2.5 99.4blas_set_num_threads(8)
peakRAM(fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=1))## 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | 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|===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. 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) 122.31
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 9.2 106.1blas_set_num_threads(1)
peakRAM(fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=8))## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 33 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% 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## 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-50. 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) 18.573
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 8.2 106.1blas_set_num_threads(4)
peakRAM(fgseaRes <- fgsea(pathways = pw,
stats = f,
minSize = 10,
nproc=4))## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 28 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)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | 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|===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%## 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-50. 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) 34.402
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 8.2 106fres <- 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
## 120.068 69.755 46.476 35.120 28.838 24.750 21.222 20.668 17.689 17.154
## 11 12 13 14 15 16
## 14.812 15.143 14.409 13.716 12.449 13.005barplot(fres,ylab="elapsed time in s",xlab="parallel threads", main="fgsea")
Session information
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## 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=en_US.UTF-8
## [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
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] peakRAM_1.0.2 RhpcBLASctl_0.23-42 tictoc_1.2.1
## [4] fgsea_1.30.0 mitch_1.16.0
##
## loaded via a namespace (and not attached):
## [1] fastmatch_1.1-4 gtable_0.3.5 beeswarm_0.4.0
## [4] xfun_0.45 bslib_0.7.0 ggplot2_3.5.1
## [7] htmlwidgets_1.6.4 caTools_1.18.2 lattice_0.22-6
## [10] GGally_2.2.1 vctrs_0.6.5 tools_4.4.1
## [13] bitops_1.0-7 generics_0.1.3 parallel_4.4.1
## [16] tibble_3.2.1 fansi_1.0.6 highr_0.11
## [19] pkgconfig_2.0.3 Matrix_1.7-0 KernSmooth_2.23-24
## [22] data.table_1.15.4 RColorBrewer_1.1-3 lifecycle_1.0.4
## [25] compiler_4.4.1 stringr_1.5.1 gplots_3.1.3.1
## [28] munsell_0.5.1 codetools_0.2-20 httpuv_1.6.15
## [31] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.8
## [34] later_1.3.2 pillar_1.9.0 jquerylib_0.1.4
## [37] tidyr_1.3.1 MASS_7.3-61 BiocParallel_1.38.0
## [40] cachem_1.1.0 mime_0.12 ggstats_0.6.0
## [43] gtools_3.9.5 tidyselect_1.2.1 digest_0.6.36
## [46] stringi_1.8.4 dplyr_1.1.4 reshape2_1.4.4
## [49] purrr_1.0.2 cowplot_1.1.3 fastmap_1.2.0
## [52] grid_4.4.1 colorspace_2.1-0 cli_3.6.3
## [55] magrittr_2.0.3 utf8_1.2.4 scales_1.3.0
## [58] promises_1.3.0 rmarkdown_2.27 gridExtra_2.3
## [61] kableExtra_1.4.0 shiny_1.8.1.1 evaluate_0.24.0
## [64] knitr_1.47 viridisLite_0.4.2 rlang_1.1.4
## [67] Rcpp_1.0.12 xtable_1.8-4 glue_1.7.0
## [70] echarts4r_0.4.5 BiocManager_1.30.23 xml2_1.3.6
## [73] svglite_2.1.3 rstudioapi_0.16.0 jsonlite_1.8.8
## [76] R6_2.5.1 plyr_1.8.9 systemfonts_1.1.0