Testing parallel processing of pathway enrichment analysis
2024-07-22
Source: TBA
Intro
How many parallel threads should be used for pathway enrichment analysis?
AMD Ryzen Threadripper 1900X 8-Core Processor (16 parallel threads).
BiocManager::install(c("getDEE2","DESeq2","mitch","fgsea"))## 'getOption("repos")' replaces Bioconductor standard repositories, see
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## (as 'lib' is unspecified)library("getDEE2")
library("DESeq2")## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics##
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## shiftlibrary("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.mdxg <- x$GeneCounts
head(xg)## SRR1171523 SRR1171524 SRR1171525 SRR1171526 SRR1171527
## ENSG00000223972 0 0 0 0 0
## ENSG00000227232 1 0 3 1 4
## ENSG00000278267 0 0 0 0 0
## ENSG00000243485 0 0 0 0 0
## ENSG00000284332 0 0 0 0 0
## ENSG00000237613 0 0 0 0 0
## SRR1171528
## ENSG00000223972 0
## ENSG00000227232 4
## ENSG00000278267 0
## ENSG00000243485 1
## ENSG00000284332 0
## ENSG00000237613 0ss <- x$MetadataSummary
ss$trt <- factor(as.numeric(grepl("Treat",ss$Experiment_title)))Update gene symbols
gnames <- x$GeneInfo[,1,drop=FALSE]
xm <- merge(gnames,xg,by=0)
rownames(xm) <- paste(xm[,1],xm[,2])
xm[,1] = xm[,2] = NULLDifferential expression
Now run differential expression analysis with DESeq2.
xmf <- xm[which(rowMeans(xm)>=10),]
dds <- DESeqDataSetFromMatrix(countData = xmf, colData = ss, design = ~ trt )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz<- DESeq2::results(res)
vsd <- vst(dds, blind=FALSE)
zz<-cbind(z,assay(vsd))
de<-as.data.frame(zz[order(zz$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 8.871748saveRDS(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 = 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.## 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 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) 49.999
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 1.1 122.4mres <- 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
## 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 17.210barplot(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)## | | | 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.## 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)## | | | 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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## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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% | |=================== | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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.## 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)## | | | 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% | |=================== | 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 pathways, in reality P-values are less than 1e-50. You can
## set the `eps` argument to zero for better estimation.## 25.378 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 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)## | | | 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.## 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 4.9 110.5blas_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)## | | | 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.## 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 13.4 118.2blas_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)## | | | 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=8) 30.15
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 9.3 118.2blas_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)## | | | 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.## 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 9.2 118.2fres <- 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 25.378barplot(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