Source: TBA
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://packagemanager.posit.co/cran/__linux__/jammy/latest
## Warning: unable to access index for repository https://bioconductor.org/packages/3.17/bioc/src/contrib:
## cannot open URL 'https://bioconductor.org/packages/3.17/bioc/src/contrib/PACKAGES'
## Bioconductor version 3.17 (BiocManager 1.30.22), R 4.3.1 (2023-06-16)
## Installing package(s) 'mitch', 'fgsea'
## also installing the dependencies 'bit', 'bit64', 'vroom', 'tzdb', 'readr', 'haven', 'labelled', 'formatR', 'generics', 'tidyselect', 'vctrs', '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: 'BiocManager', 'brew', 'brio', 'bslib', 'cachem', 'callr', 'cli',
## 'commonmark', 'cpp11', 'crayon', 'curl', 'desc', 'digest', 'downlit',
## 'evaluate', 'fansi', 'fastmap', 'fs', 'gert', 'gh', 'glue', 'highr',
## 'htmltools', 'htmlwidgets', 'httpuv', 'httr2', 'jsonlite', 'knitr', 'later',
## 'lifecycle', 'littler', 'openssl', 'pkgbuild', 'pkgdown', 'pkgload',
## 'prettyunits', 'processx', 'promises', 'ps', 'ragg', 'Rcpp', 'remotes',
## 'rlang', 'roxygen2', 'rprojroot', 'rstudioapi', 'sass', 'shiny', 'stringi',
## 'stringr', 'systemfonts', 'testthat', 'textshaping', 'tinytex', 'usethis',
## 'utf8', 'waldo', 'withr', 'xfun', 'xml2', 'xopen', 'yaml', 'zip', 'boot',
## 'cluster', 'codetools', 'foreign', 'KernSmooth', 'lattice', 'mgcv', 'nlme',
## 'rpart', 'survival'
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)
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
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")
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.
## 44.605 sec elapsed
## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.
## 26.483 sec elapsed
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## p-values), large effect sizes might be missed.
## 19.674 sec elapsed
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## p-values), large effect sizes might be missed.
## 17.2 sec elapsed
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## p-values), large effect sizes might be missed.
## 17.198 sec elapsed
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## p-values), large effect sizes might be missed.
## 14.894 sec elapsed
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## p-values), large effect sizes might be missed.
## 14.071 sec elapsed
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## p-values), large effect sizes might be missed.
## 14.369 sec elapsed
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## p-values), large effect sizes might be missed.
## 15.055 sec elapsed
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## p-values), large effect sizes might be missed.
## 14.159 sec elapsed
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## p-values), large effect sizes might be missed.
## 14.574 sec elapsed
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## p-values), large effect sizes might be missed.
## 14.364 sec elapsed
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## p-values), large effect sizes might be missed.
## 13.495 sec elapsed
## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.
## 14.658 sec elapsed
## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.
## 13.781 sec elapsed
## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.
## 13.253 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) 41.158
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 1 110.5
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
## 44.605 26.483 19.674 17.200 17.198 14.894 14.071 14.369 15.055 14.159 14.574
## 12 13 14 15 16
## 14.364 13.495 14.658 13.781 13.253
barplot(mres,ylab="elapsed time in s",xlab="parallel threads", main="mitch")
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 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 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.
## 152.747 sec elapsed
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 26 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
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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.
## 84.488 sec elapsed
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 29 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
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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.
## 58.438 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.
## 44.766 sec elapsed
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 27 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | 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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.
## 38.612 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% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | 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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.
## 34.038 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% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | 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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.
## 28.667 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% | |========== | 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% | |====================== | 31% | 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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.
## 26.981 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% | |============ | 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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## 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.
## 24.85 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% | |========== | 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% | |====================== | 31% | 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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.
## 24.733 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.
## 24.942 sec elapsed
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 16 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 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% | |===================== | 30% | 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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.
## 23.266 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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## 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.
## 22.01 sec elapsed
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 23 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 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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.
## 24.102 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% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | 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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.
## 21.013 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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## 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.
## 20.953 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) 141.987
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 4.2 99.6
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 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.
## Function_Call Elapsed_Time_sec
## 1 fgseaRes<-fgsea(pathways=pw,stats=f,minSize=10,nproc=1) 137.885
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 10.6 106.2
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 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=8) 26.823
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 8.3 106.2
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 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% | |====================== | 31% | 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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) 46.987
## Total_RAM_Used_MiB Peak_RAM_Used_MiB
## 1 8.3 106.2
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
## 152.747 84.488 58.438 44.766 38.612 34.038 28.667 26.981 24.850 24.733
## 11 12 13 14 15 16
## 24.942 23.266 22.010 24.102 21.013 20.953
barplot(fres,ylab="elapsed time in s",xlab="parallel threads", main="fgsea")
sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 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_1.26.0 mitch_1.12.0
##
## loaded via a namespace (and not attached):
## [1] fastmatch_1.1-4 gtable_0.3.5 beeswarm_0.4.0
## [4] xfun_0.40 bslib_0.5.1 ggplot2_3.5.1
## [7] htmlwidgets_1.6.2 caTools_1.18.2 lattice_0.21-8
## [10] GGally_2.2.1 vctrs_0.6.5 tools_4.3.1
## [13] bitops_1.0-7 generics_0.1.3 parallel_4.3.1
## [16] tibble_3.2.1 fansi_1.0.4 highr_0.10
## [19] pkgconfig_2.0.3 Matrix_1.6-1 KernSmooth_2.23-22
## [22] data.table_1.15.4 RColorBrewer_1.1-3 lifecycle_1.0.3
## [25] compiler_4.3.1 stringr_1.5.0 gplots_3.1.3.1
## [28] munsell_0.5.1 codetools_0.2-19 httpuv_1.6.11
## [31] htmltools_0.5.6 sass_0.4.7 yaml_2.3.7
## [34] later_1.3.1 pillar_1.9.0 jquerylib_0.1.4
## [37] tidyr_1.3.1 MASS_7.3-60 ellipsis_0.3.2
## [40] BiocParallel_1.34.2 cachem_1.0.8 mime_0.12
## [43] ggstats_0.6.0 gtools_3.9.5 tidyselect_1.2.1
## [46] digest_0.6.33 stringi_1.7.12 dplyr_1.1.4
## [49] reshape2_1.4.4 purrr_1.0.2 cowplot_1.1.3
## [52] fastmap_1.1.1 grid_4.3.1 colorspace_2.1-0
## [55] cli_3.6.1 magrittr_2.0.3 utf8_1.2.3
## [58] scales_1.3.0 promises_1.2.1 rmarkdown_2.27
## [61] gridExtra_2.3 kableExtra_1.4.0 shiny_1.7.5
## [64] evaluate_0.21 knitr_1.43 viridisLite_0.4.2
## [67] rlang_1.1.1 Rcpp_1.0.11 xtable_1.8-4
## [70] glue_1.6.2 echarts4r_0.4.5 BiocManager_1.30.22
## [73] xml2_1.3.5 svglite_2.1.3 rstudioapi_0.15.0
## [76] jsonlite_1.8.7 R6_2.5.1 plyr_1.8.9
## [79] systemfonts_1.0.4