Gene set testing for Illumina HumanMethylation Arrays
Comparing the run-time of different methods
Jovana Maksimovic, Alicia Oshlack and Belinda Phipson
May 21, 2021
Last updated: 2021-05-21
Checks: 7 0
Knit directory: methyl-geneset-testing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d9f6766 | JovMaksimovic | 2021-05-21 | wflow_publish("analysis/06_runTimeComparison.Rmd") |
| html | 8511442 | Jovana Maksimovic | 2021-05-17 | Build site. |
| Rmd | b6cde69 | Jovana Maksimovic | 2021-05-17 | Added code for running methods on 10 and 20 cores. |
| html | f148103 | Jovana Maksimovic | 2021-04-20 | Build site. |
| Rmd | 2eefa78 | Jovana Maksimovic | 2021-04-20 | wflow_publish("analysis/06_runTimeComparison.Rmd") |
| html | 4c57176 | JovMaksimovic | 2021-04-13 | Build site. |
| Rmd | 2a8ac36 | JovMaksimovic | 2021-04-13 | Changed RNAseq to Array for B-cell data analyses |
| Rmd | 1cf068b | Jovana Maksimovic | 2021-04-12 | Updated run time comparison with ebGSEA |
| Rmd | 1a12923 | JovMaksimovic | 2021-03-30 | Update ebgsea function used |
| html | bbf77e1 | JovMaksimovic | 2020-08-21 | Build site. |
| Rmd | e3b14f4 | JovMaksimovic | 2020-08-21 | wflow_publish(c("analysis/01_exploreArrayBiasEPIC.Rmd", "analysis/02_exploreArrayBias450.Rmd", |
| html | 555069b | JovMaksimovic | 2020-08-14 | Build site. |
| Rmd | 91699a8 | JovMaksimovic | 2020-08-14 | wflow_publish("analysis/_site.yml", republish = TRUE, all = TRUE) |
| Rmd | 39bdd22 | JovMaksimovic | 2020-08-14 | Renamed analysis files with numbering for run order. |
library(here)
library(minfi)
library(limma)
library(reshape2)
library(missMethyl)
library(ggplot2)
library(glue)
library(tidyverse)
library(patchwork)
library(ChAMPdata)
library(tictoc)
library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
library(EnsDb.Hsapiens.v75)
library(ChAMP)
library(ebGSEA)
library(methylGSA)
library(gt)
source(here("code/utility.R"))Compare run-time of different methods
Setup inputs
Create database for translating gene IDs.
edb <- EnsDb.Hsapiens.v75
transIDs <- genes(edb, columns = c("symbol", "gene_id", "entrezid"),
return.type = "DataFrame")Measure run-times for different methods
Execute and record run-time for each method (on a single core) for the three different contrasts.
outDir <- here::here("data/cache-runtime")
if (!dir.exists(outDir)) dir.create(outDir)
inFile <- here("data/cache-runtime/run-time-results.rds")
if(!file.exists(inFile)){
data("PathwayList")
keep <- sapply(PathwayList, function(x) any(x %in% transIDs$symbol))
symbol <- suppressMessages(lapply(PathwayList[keep], function(x){
tmp <- unlist(transIDs$symbol[transIDs$symbol %in% x], use.names = FALSE)
tmp[!is.na(tmp)]
}))
entrezid <- suppressMessages(lapply(symbol, function(x){
tmp <- unlist(transIDs$entrezid[transIDs$symbol %in% x], use.names = FALSE)
tmp[!is.na(tmp)]
}))
load(here("data/cache-intermediates/input.RData"))
bVals <- ilogit2(mVals)
anno <- minfi::getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
timing <- NULL
for(i in 1:ncol(tfit$contrasts)){
top <- topTreat(tfit, coef = i, number = 5000)
tic("gometh")
res <- gsameth(sig.cpg = rownames(top),
all.cpg = rownames(tfit$coefficients), collection = entrezid,
array.type = "EPIC", anno = anno)
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
timing <- bind_rows(timing, log)
tic("mgsa.glm")
res <- methylglm(cpg.pval = tfit$p.value[,i],
FullAnnot = anno, minsize = minsize, maxsize = maxsize,
GS.list = symbol, GS.idtype = "SYMBOL")
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
timing <- bind_rows(timing, log)
tic("mgsa.ora")
res <- methylRRA(cpg.pval = tfit$p.value[,i],
method = "ORA", FullAnnot = anno, minsize = minsize,
maxsize = maxsize, GS.list = symbol,
GS.idtype = "SYMBOL")
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
timing <- bind_rows(timing, log)
tic("mgsa.gsea")
res <- methylRRA(cpg.pval = tfit$p.value[,i],
method = "GSEA", FullAnnot = anno, minsize = minsize,
maxsize = maxsize, GS.list = symbol,
GS.idtype = "SYMBOL")
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
timing <- bind_rows(timing, log)
cellType <- names(tfit$contrasts[,i])[tfit$contrasts[,i] != 0]
tic("ebgsea")
#tic("champ.ebgsea")
# ebgs <- champ.ebGSEA(beta = mVals[,targets$CellType %in% cellType],
# pheno = targets$CellType[targets$CellType %in% cellType],
# minN = 5, adjPval=1, arraytype = "EPIC")
groups <- names(tfit$contrasts[,i])[tfit$contrasts[,i] != 0]
samps <- as.logical(unname(rowSums(tfit$design[,groups])))
pheno <- unname(tfit$design[samps, groups][,1])
gtRanks <- doGT(pheno.v = pheno,
data.m = bVals[, samps],
array = "EPIC",
ncores = 1)
ebgs <- data.frame(doGSEAwt(rankEID.m = gtRanks, ptw.ls = entrezid,
ncores = 1, minN = minsize, adjPVth = 1)$`Rank(P)`)
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
timing <- bind_rows(timing, log)
}
saveRDS(timing, file = inFile)
} else {
timing <- readRDS(inFile)
colnames(timing)[3] <- "contrast"
}Visualise results
Plot run-time results.
timing %>% mutate(time = as.integer(time)) %>%
mutate(method = unname(dict[method])) -> dat
p1 <- ggplot(dat, aes(x = reorder(method, -time), y = time/60,
fill = contrast)) +
geom_bar(position = "dodge", stat = "identity") +
labs(x = "Method", y = "Run time (minutes)", fill = "Contrast")
p1
inFile <- here("data/cache-runtime/run-time-mcores-ebgsea.rds")
if(!file.exists(inFile)){
data("PathwayList")
keep <- sapply(PathwayList, function(x) any(x %in% transIDs$symbol))
symbol <- suppressMessages(lapply(PathwayList[keep], function(x){
tmp <- unlist(transIDs$symbol[transIDs$symbol %in% x], use.names = FALSE)
tmp[!is.na(tmp)]
}))
entrezid <- suppressMessages(lapply(symbol, function(x){
tmp <- unlist(transIDs$entrezid[transIDs$symbol %in% x], use.names = FALSE)
tmp[!is.na(tmp)]
}))
load(here("data/cache-intermediates/input.RData"))
bVals <- ilogit2(mVals)
anno <- minfi::getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
multi <- NULL
for(i in 1:ncol(tfit$contrasts)){
tic("ebgsea.10")
groups <- names(tfit$contrasts[,i])[tfit$contrasts[,i] != 0]
samps <- as.logical(unname(rowSums(tfit$design[,groups])))
pheno <- unname(tfit$design[samps, groups][,1])
gtRanks <- doGT(pheno.v = pheno,
data.m = bVals[, samps],
array = "EPIC",
ncores = 10)
ebgs <- data.frame(doGSEAwt(rankEID.m = gtRanks, ptw.ls = entrezid,
ncores = 10, minN = minsize, adjPVth = 1)$`Rank(P)`)
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
multi <- bind_rows(multi, log)
tic("ebgsea.20")
groups <- names(tfit$contrasts[,i])[tfit$contrasts[,i] != 0]
samps <- as.logical(unname(rowSums(tfit$design[,groups])))
pheno <- unname(tfit$design[samps, groups][,1])
gtRanks <- doGT(pheno.v = pheno,
data.m = bVals[, samps],
array = "EPIC",
ncores = 20)
ebgs <- data.frame(doGSEAwt(rankEID.m = gtRanks, ptw.ls = entrezid,
ncores = 20, minN = minsize, adjPVth = 1)$`Rank(P)`)
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
multi <- bind_rows(multi, log)
}
saveRDS(multi, file = inFile)
} else {
multi <- readRDS(inFile)
}Plot run-time results.
multi %>% mutate(time = as.integer(time)) %>%
mutate(cores = limma::strsplit2(method, ".", fixed=TRUE)[,2]) -> ebgseaMulti
p2 <- ggplot(ebgseaMulti, aes(x = cores, y = time/60, fill = contrast)) +
geom_bar(position = "dodge", stat = "identity") +
labs(x = "No. cores", y = "Run time (minutes)", fill = "Contrast") +
ggtitle("Using multiple cores for ebGSEA")
p2
inFile <- here("data/cache-runtime/run-time-mcores.rds")
if(!file.exists(inFile)){
data("PathwayList")
keep <- sapply(PathwayList, function(x) any(x %in% transIDs$symbol))
symbol <- suppressMessages(lapply(PathwayList[keep], function(x){
tmp <- unlist(transIDs$symbol[transIDs$symbol %in% x], use.names = FALSE)
tmp[!is.na(tmp)]
}))
load(here("data/cache-intermediates/input.RData"))
bVals <- ilogit2(mVals)
anno <- minfi::getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
multi <- NULL
for(i in 1:ncol(tfit$contrasts)){
tic("mgsa.glm.10")
res <- methylglm(cpg.pval = tfit$p.value[,i],
FullAnnot = anno, minsize = minsize, maxsize = maxsize,
GS.list = symbol, GS.idtype = "SYMBOL", parallel = TRUE,
BPPARAM = BiocParallel::MulticoreParam(workers = 10))
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
multi <- bind_rows(multi, log)
tic("mgsa.glm.20")
res <- methylglm(cpg.pval = tfit$p.value[,i],
FullAnnot = anno, minsize = minsize, maxsize = maxsize,
GS.list = symbol, GS.idtype = "SYMBOL", parallel = TRUE,
BPPARAM = BiocParallel::MulticoreParam(workers = 20))
toc(log = TRUE, quiet = TRUE)
tmp <- strsplit2(tic.log(format = TRUE)[[1]], " ")
log <- data.frame(method = gsub(":", "", tmp[1]), time = tmp[2],
contrast = colnames(tfit$contrasts)[i])
tic.clearlog()
multi <- bind_rows(multi, log)
}
saveRDS(multi, file = inFile)
} else {
multi <- readRDS(inFile)
}Plot run-time results.
multi %>% mutate(time = as.integer(time)) %>%
mutate(cores = limma::strsplit2(method, ".", fixed=TRUE)[,3]) -> mglmMulti
p3 <- ggplot(mglmMulti, aes(x = cores, y = time/60, fill = contrast)) +
geom_bar(position = "dodge", stat = "identity") +
labs(x = "No. cores", y = "Run time (minutes)", fill = "Contrast") +
ggtitle("Using multiple cores for mGLM")
p3
Combining single core and multi-core plots.
p1 / (p2 | p3) + plot_layout(guides = "collect") &
theme(text = element_text(size = 8))
Run-time summary table
timing$cores <- 1
timing$time <- as.integer(timing$time)
ebgseaMulti$cores <- as.integer(ebgseaMulti$cores)
mglmMulti$cores <- as.integer(mglmMulti$cores)
timing <- bind_rows(timing, ebgseaMulti, mglmMulti)
timing$method[grepl("mgsa.glm",timing$method)] <- "mgsa.glm"
timing$method[grepl("ebgsea",timing$method)] <- "ebgsea"
timing %>% mutate(method = unname(dict[method])) %>%
group_by(method, cores) %>%
mutate(time = as.integer(time)) %>%
summarise(mean = mean(time)) %>%
mutate(mins = mean/60) %>%
dplyr::select(method, cores, mins) %>%
arrange(cores, mins) %>%
as_tibble() -> tab`summarise()` has grouped output by 'method'. You can override using the `.groups` argument.tab %>% gt() %>%
fmt_number(columns = vars(mins), decimals = 2) %>%
cols_label(
mins = md("**Minutes**"),
cores = md("**Cores**"),
method = md("**Method**")
) %>%
tab_header(
title = md("**Average run-time across all contrasts**"),
subtitle = md("Using Broad MSigDB gene sets from `ChAMP` package")
) %>%
tab_source_note(md("_*Blade Server: 24 CPUs - Intel(R) Xeon(R) Gold 6126 CPU @ 2.60GHz._"))| Average run-time across all contrasts | ||
|---|---|---|
Using Broad MSigDB gene sets from ChAMP package |
||
| Method | Cores | Minutes |
| mRRA (ORA) | 1 | 0.13 |
| GOmeth | 1 | 0.54 |
| mRRA (GSEA) | 1 | 3.04 |
| mGLM | 1 | 47.95 |
| ebGSEA | 1 | 50.12 |
| mGLM | 10 | 16.12 |
| ebGSEA | 10 | 36.07 |
| mGLM | 20 | 9.86 |
| ebGSEA | 20 | 33.79 |
| *Blade Server: 24 CPUs - Intel(R) Xeon(R) Gold 6126 CPU @ 2.60GHz. | ||
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] gt_0.2.2
[2] methylGSA_1.8.0
[3] ebGSEA_0.1.0
[4] ChAMP_2.20.1
[5] RPMM_1.25
[6] cluster_2.1.1
[7] DT_0.17
[8] IlluminaHumanMethylationEPICmanifest_0.3.0
[9] Illumina450ProbeVariants.db_1.26.0
[10] DMRcate_2.4.1
[11] EnsDb.Hsapiens.v75_2.99.0
[12] ensembldb_2.14.0
[13] AnnotationFilter_1.14.0
[14] GenomicFeatures_1.42.3
[15] AnnotationDbi_1.52.0
[16] tictoc_1.0
[17] ChAMPdata_2.22.0
[18] patchwork_1.1.1
[19] forcats_0.5.1
[20] stringr_1.4.0
[21] dplyr_1.0.5
[22] purrr_0.3.4
[23] readr_1.4.0
[24] tidyr_1.1.3
[25] tibble_3.1.0
[26] tidyverse_1.3.0
[27] glue_1.4.2
[28] ggplot2_3.3.3
[29] missMethyl_1.24.0
[30] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
[31] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
[32] reshape2_1.4.4
[33] limma_3.46.0
[34] minfi_1.36.0
[35] bumphunter_1.32.0
[36] locfit_1.5-9.4
[37] iterators_1.0.13
[38] foreach_1.5.1
[39] Biostrings_2.58.0
[40] XVector_0.30.0
[41] SummarizedExperiment_1.20.0
[42] Biobase_2.50.0
[43] MatrixGenerics_1.2.1
[44] matrixStats_0.58.0
[45] GenomicRanges_1.42.0
[46] GenomeInfoDb_1.26.7
[47] IRanges_2.24.1
[48] S4Vectors_0.28.1
[49] BiocGenerics_0.36.0
[50] here_1.0.1
[51] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Hmisc_4.5-0
[2] Rsamtools_2.6.0
[3] rprojroot_2.0.2
[4] crayon_1.4.1
[5] MASS_7.3-53.1
[6] rhdf5filters_1.2.0
[7] nlme_3.1-152
[8] backports_1.2.1
[9] reprex_2.0.0
[10] sva_3.38.0
[11] impute_1.64.0
[12] GOSemSim_2.16.1
[13] rlang_0.4.10
[14] readxl_1.3.1
[15] DSS_2.38.0
[16] BiocParallel_1.24.1
[17] globaltest_5.44.0
[18] bit64_4.0.5
[19] isva_1.9
[20] rngtools_1.5
[21] methylumi_2.36.0
[22] DOSE_3.16.0
[23] haven_2.3.1
[24] tidyselect_1.1.0
[25] XML_3.99-0.6
[26] nleqslv_3.3.2
[27] GenomicAlignments_1.26.0
[28] xtable_1.8-4
[29] magrittr_2.0.1
[30] evaluate_0.14
[31] cli_2.4.0
[32] zlibbioc_1.36.0
[33] rstudioapi_0.13
[34] doRNG_1.8.2
[35] whisker_0.4
[36] bslib_0.2.4
[37] rpart_4.1-15
[38] fastmatch_1.1-0
[39] shiny_1.6.0
[40] xfun_0.22
[41] askpass_1.1
[42] clue_0.3-58
[43] multtest_2.46.0
[44] tidygraph_1.2.0
[45] interactiveDisplayBase_1.28.0
[46] ggrepel_0.9.1
[47] base64_2.0
[48] biovizBase_1.38.0
[49] scrime_1.3.5
[50] dendextend_1.14.0
[51] png_0.1-7
[52] permute_0.9-5
[53] reshape_0.8.8
[54] withr_2.4.1
[55] ggforce_0.3.3
[56] lumi_2.42.0
[57] bitops_1.0-6
[58] plyr_1.8.6
[59] cellranger_1.1.0
[60] JADE_2.0-3
[61] pillar_1.5.1
[62] cachem_1.0.4
[63] fs_1.5.0
[64] clusterProfiler_3.18.1
[65] DelayedMatrixStats_1.12.3
[66] vctrs_0.3.7
[67] ellipsis_0.3.1
[68] generics_0.1.0
[69] tools_4.0.3
[70] foreign_0.8-81
[71] tweenr_1.0.2
[72] munsell_0.5.0
[73] fgsea_1.16.0
[74] DelayedArray_0.16.3
[75] fastmap_1.1.0
[76] compiler_4.0.3
[77] httpuv_1.5.5
[78] rtracklayer_1.50.0
[79] geneLenDataBase_1.26.0
[80] ExperimentHub_1.16.0
[81] beanplot_1.2
[82] Gviz_1.34.1
[83] plotly_4.9.3
[84] GenomeInfoDbData_1.2.4
[85] gridExtra_2.3
[86] DNAcopy_1.64.0
[87] edgeR_3.32.1
[88] lattice_0.20-41
[89] utf8_1.2.1
[90] later_1.1.0.1
[91] RobustRankAggreg_1.1
[92] BiocFileCache_1.14.0
[93] jsonlite_1.7.2
[94] affy_1.68.0
[95] scales_1.1.1
[96] sparseMatrixStats_1.2.1
[97] genefilter_1.72.1
[98] lazyeval_0.2.2
[99] promises_1.2.0.1
[100] doParallel_1.0.16
[101] latticeExtra_0.6-29
[102] R.utils_2.10.1
[103] goseq_1.42.0
[104] checkmate_2.0.0
[105] cowplot_1.1.1
[106] rmarkdown_2.7
[107] nor1mix_1.3-0
[108] statmod_1.4.35
[109] siggenes_1.64.0
[110] downloader_0.4
[111] dichromat_2.0-0
[112] BSgenome_1.58.0
[113] igraph_1.2.6
[114] HDF5Array_1.18.1
[115] bsseq_1.26.0
[116] survival_3.2-10
[117] yaml_2.2.1
[118] htmltools_0.5.1.1
[119] memoise_2.0.0
[120] VariantAnnotation_1.36.0
[121] graphlayouts_0.7.1
[122] quadprog_1.5-8
[123] viridisLite_0.3.0
[124] digest_0.6.27
[125] assertthat_0.2.1
[126] commonmark_1.7
[127] mime_0.10
[128] rappdirs_0.3.3
[129] BiasedUrn_1.07
[130] RSQLite_2.2.5
[131] data.table_1.14.0
[132] blob_1.2.1
[133] R.oo_1.24.0
[134] preprocessCore_1.52.1
[135] labeling_0.4.2
[136] fastICA_1.2-2
[137] shinythemes_1.2.0
[138] splines_4.0.3
[139] Formula_1.2-4
[140] Rhdf5lib_1.12.1
[141] illuminaio_0.32.0
[142] AnnotationHub_2.22.0
[143] ProtGenerics_1.22.0
[144] RCurl_1.98-1.3
[145] broom_0.7.6
[146] hms_1.0.0
[147] modelr_0.1.8
[148] rhdf5_2.34.0
[149] colorspace_2.0-0
[150] base64enc_0.1-3
[151] BiocManager_1.30.12
[152] nnet_7.3-15
[153] sass_0.3.1
[154] GEOquery_2.58.0
[155] Rcpp_1.0.6
[156] mclust_5.4.7
[157] enrichplot_1.10.2
[158] fansi_0.4.2
[159] R6_2.5.0
[160] grid_4.0.3
[161] lifecycle_1.0.0
[162] curl_4.3
[163] kpmt_0.1.0
[164] affyio_1.60.0
[165] jquerylib_0.1.3
[166] DO.db_2.9
[167] Matrix_1.3-2
[168] qvalue_2.22.0
[169] ROC_1.66.0
[170] org.Hs.eg.db_3.12.0
[171] RColorBrewer_1.1-2
[172] IlluminaHumanMethylation450kmanifest_0.4.0
[173] htmlwidgets_1.5.3
[174] polyclip_1.10-0
[175] biomaRt_2.46.3
[176] shadowtext_0.0.7
[177] reactome.db_1.74.0
[178] marray_1.68.0
[179] rvest_1.0.0
[180] mgcv_1.8-34
[181] openssl_1.4.3
[182] htmlTable_2.1.0
[183] codetools_0.2-18
[184] lubridate_1.7.10
[185] GO.db_3.12.1
[186] gtools_3.8.2
[187] prettyunits_1.1.1
[188] dbplyr_2.1.1
[189] R.methodsS3_1.8.1
[190] gtable_0.3.0
[191] DBI_1.1.1
[192] git2r_0.28.0
[193] wateRmelon_1.34.0
[194] highr_0.8
[195] httr_1.4.2
[196] KernSmooth_2.23-18
[197] stringi_1.5.3
[198] progress_1.2.2
[199] farver_2.1.0
[200] annotate_1.68.0
[201] viridis_0.5.1
[202] xml2_1.3.2
[203] combinat_0.0-8
[204] rvcheck_0.1.8
[205] BiocVersion_3.12.0
[206] bit_4.0.4
[207] scatterpie_0.1.5
[208] jpeg_0.1-8.1
[209] ggraph_2.0.5
[210] pkgconfig_2.0.3
[211] knitr_1.31