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"))
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## Bioconductor version 3.19 (BiocManager 1.30.23), R 4.4.1 (2024-06-14)
## Warning: package(s) not installed when version(s) same as or greater than current; use
##   `force = TRUE` to re-install: 'getDEE2' 'DESeq2' 'mitch' 'fgsea'
## Installation paths not writeable, unable to update packages
##   path: /usr/local/lib/R/site-library
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## Old packages: 'limma'
install.packages(c("tictoc","RhpcBLASctl","peakRAM"))
## Installing packages into '/home/mdz/R/x86_64-pc-linux-gnu-library/4.4'
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library("getDEE2")
library("DESeq2")
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library("mitch")
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library("fgsea")
library("tictoc")
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library("RhpcBLASctl")
library("peakRAM")
blas_set_num_threads(1)

Get gene expression data

And set up sample sheet.

SRRvec <- c("SRR1171523","SRR1171524","SRR1171525","SRR1171526","SRR1171527","SRR1171528")
x <- getDEE2("hsapiens", SRRvec, outfile="NULL", counts="GeneCounts", legacy=TRUE)
## For more information about DEE2 QC metrics, visit
##     https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md
xg <- x$GeneCounts

head(xg)
##                 SRR1171523 SRR1171524 SRR1171525 SRR1171526 SRR1171527
## ENSG00000223972          0          0          0          0          0
## ENSG00000227232          1          0          3          1          4
## ENSG00000278267          0          0          0          0          0
## ENSG00000243485          0          0          0          0          0
## ENSG00000284332          0          0          0          0          0
## ENSG00000237613          0          0          0          0          0
##                 SRR1171528
## ENSG00000223972          0
## ENSG00000227232          4
## ENSG00000278267          0
## ENSG00000243485          1
## ENSG00000284332          0
## ENSG00000237613          0
ss <- x$MetadataSummary
ss$trt <- factor(as.numeric(grepl("Treat",ss$Experiment_title)))

Update gene symbols

gnames <- x$GeneInfo[,1,drop=FALSE]
xm <- merge(gnames,xg,by=0)
rownames(xm) <- paste(xm[,1],xm[,2])
xm[,1] = xm[,2] = NULL

Differential expression

Now run differential expression analysis with DESeq2.

xmf <- xm[which(rowMeans(xm)>=10),]

dds <- DESeqDataSetFromMatrix(countData = xmf, colData = ss, design = ~ trt )
res <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
z<- DESeq2::results(res)
vsd <- vst(dds, blind=FALSE)
zz<-cbind(z,assay(vsd))
de<-as.data.frame(zz[order(zz$padj),])

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
saveRDS(de,"de.Rds")

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.
## 51.484 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 31.895 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 26.249 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 22.593 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 16.638 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 19.104 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 17.12 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 16.705 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 19.511 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 16.975 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 17.375 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 17.752 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 17.836 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 16.778 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 18.898 sec elapsed
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
## 17.21 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)           49.999
##   Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1                1.1             122.4
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 
## 51.484 31.895 26.249 22.593 16.638 19.104 17.120 16.705 19.511 16.975 17.375 
##     12     13     14     15     16 
## 17.752 17.836 16.778 18.898 17.210
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 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.
## 142.214 sec elapsed
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 22 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.
## 90.218 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.
## 60.015 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.
## 48.615 sec elapsed
## 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.
## 39.267 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.
## 36.014 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)
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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.
## 30.777 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.
## 30.27 sec elapsed
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 18 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.
## 28.4 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.
## 29.03 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.
## 26.8 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.
## 28.78 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.
## 25.25 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.
## 26.086 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.
## 27.394 sec elapsed
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 13 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.
## 25.378 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 22 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)          142.301
##   Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1                4.9             110.5
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 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.
##                                             Function_Call Elapsed_Time_sec
## 1 fgseaRes<-fgsea(pathways=pw,stats=f,minSize=10,nproc=1)           151.74
##   Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1               13.4             118.2
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 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.
##                                             Function_Call Elapsed_Time_sec
## 1 fgseaRes<-fgsea(pathways=pw,stats=f,minSize=10,nproc=8)            30.15
##   Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1                9.3             118.2
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 22 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)            48.46
##   Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1                9.2             118.2
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 
## 142.214  90.218  60.015  48.615  39.267  36.014  30.777  30.270  28.400  29.030 
##      11      12      13      14      15      16 
##  26.800  28.780  25.250  26.086  27.394  25.378
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/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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: Australia/Melbourne
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] peakRAM_1.0.2               RhpcBLASctl_0.23-42        
##  [3] tictoc_1.2.1                fgsea_1.30.0               
##  [5] mitch_1.17.1                DESeq2_1.44.0              
##  [7] SummarizedExperiment_1.34.0 Biobase_2.64.0             
##  [9] MatrixGenerics_1.16.0       matrixStats_1.3.0          
## [11] GenomicRanges_1.56.1        GenomeInfoDb_1.40.1        
## [13] IRanges_2.38.1              S4Vectors_0.42.1           
## [15] BiocGenerics_0.50.0         getDEE2_1.14.0             
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7            gridExtra_2.3           echarts4r_0.4.5        
##  [4] rlang_1.1.4             magrittr_2.0.3          compiler_4.4.1         
##  [7] systemfonts_1.1.0       vctrs_0.6.5             reshape2_1.4.4         
## [10] htm2txt_2.2.2           stringr_1.5.1           pkgconfig_2.0.3        
## [13] crayon_1.5.3            fastmap_1.2.0           XVector_0.44.0         
## [16] caTools_1.18.2          utf8_1.2.4              promises_1.3.0         
## [19] rmarkdown_2.27          UCSC.utils_1.0.0        purrr_1.0.2            
## [22] xfun_0.46               zlibbioc_1.50.0         cachem_1.1.0           
## [25] jsonlite_1.8.8          highr_0.11              later_1.3.2            
## [28] DelayedArray_0.30.1     BiocParallel_1.38.0     parallel_4.4.1         
## [31] R6_2.5.1                bslib_0.7.0             stringi_1.8.4          
## [34] RColorBrewer_1.1-3      GGally_2.2.1            jquerylib_0.1.4        
## [37] Rcpp_1.0.12             knitr_1.47              httpuv_1.6.15          
## [40] Matrix_1.7-0            tidyselect_1.2.1        rstudioapi_0.16.0      
## [43] abind_1.4-5             yaml_2.3.8              gplots_3.1.3.1         
## [46] codetools_0.2-20        lattice_0.22-6          tibble_3.2.1           
## [49] plyr_1.8.9              shiny_1.8.1.1           evaluate_0.24.0        
## [52] ggstats_0.6.0           xml2_1.3.6              pillar_1.9.0           
## [55] BiocManager_1.30.23     KernSmooth_2.23-24      generics_0.1.3         
## [58] ggplot2_3.5.1           munsell_0.5.1           scales_1.3.0           
## [61] gtools_3.9.5            xtable_1.8-4            glue_1.7.0             
## [64] tools_4.4.1             data.table_1.15.4       locfit_1.5-9.10        
## [67] fastmatch_1.1-4         cowplot_1.1.3           grid_4.4.1             
## [70] tidyr_1.3.1             colorspace_2.1-0        GenomeInfoDbData_1.2.12
## [73] beeswarm_0.4.0          cli_3.6.3               kableExtra_1.4.0       
## [76] fansi_1.0.6             S4Arrays_1.4.1          viridisLite_0.4.2      
## [79] svglite_2.1.3           dplyr_1.1.4             gtable_0.3.5           
## [82] sass_0.4.9              digest_0.6.36           SparseArray_1.4.8      
## [85] htmlwidgets_1.6.4       htmltools_0.5.8.1       lifecycle_1.0.4        
## [88] httr_1.4.7              mime_0.12               MASS_7.3-61