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   ggplot2
library("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   8.871748

Get 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 = 1
corerange <- 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 elapsed
peakRAM(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             110.4
mres <- 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  9.023
barplot(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)
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## 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)
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## 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)
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## 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)
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## 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.
## 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)
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## 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%  |                                               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## 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.
## 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)
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## 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)
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## 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)
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## 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)
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## 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)
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## 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)
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## 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)
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## 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)
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## 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)
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## 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)
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## 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 elapsed
blas_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)
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## 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)          121.843
##   Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1                2.5              99.4
blas_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)
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## 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             106.1
blas_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)
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## 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=8)           18.573
##   Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1                8.2             106.1
blas_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)
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## 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               106
fres <- 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  13.005
barplot(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