Gene set testing for Illumina HumanMethylation Arrays
Jovana Maksimovic
6/21/2019
Last updated: 2020-05-11
Checks: 7 0
Knit directory: methyl-geneset-testing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 25e58bc | JovMaksimovic | 2020-05-11 | Added BH to pdjust calls, added methylation vs expression plot. |
| html | 9d793dc | JovMaksimovic | 2020-04-28 | Build site. |
| Rmd | 10ef7d4 | JovMaksimovic | 2020-04-28 | wflow_publish(“analysis/regionAnalysis.Rmd”) |
| html | d930b45 | JovMaksimovic | 2020-04-27 | Build site. |
| Rmd | bd39053 | JovMaksimovic | 2020-04-27 | wflow_publish(c(“analysis/exploreData.Rmd”, “analysis/regionAnalysis.Rmd”)) |
| Rmd | cc2c73c | Jovana Maksimovic | 2020-04-21 | Updated region analysis using tidyverse functions. |
| Rmd | d7cd66e | Jovana Maksimovic | 2020-03-02 | Initial Commit |
library(here)
library(ChAMP)Warning: replacing previous import 'igraph::edges' by 'graph::edges' when
loading 'FEM'Warning: replacing previous import 'igraph::intersection' by
'graph::intersection' when loading 'FEM'Warning: replacing previous import 'igraph::degree' by 'graph::degree' when
loading 'FEM'Warning: replacing previous import 'igraph::union' by 'graph::union' when
loading 'FEM'Warning: replacing previous import 'limma::plotMA' by 'BiocGenerics::plotMA'
when loading 'FEM'Warning: replacing previous import 'igraph::path' by 'BiocGenerics::path' when
loading 'FEM'Warning: replacing previous import 'Matrix::which' by 'BiocGenerics::which' when
loading 'FEM'Warning: replacing previous import 'igraph::normalize' by
'BiocGenerics::normalize' when loading 'FEM'Warning: replacing previous import 'minfi::getMeth' by 'bsseq::getMeth' when
loading 'DMRcate'Warning: replacing previous import 'plyr::summarise' by 'plotly::summarise' when
loading 'ChAMP'Warning: replacing previous import 'plyr::rename' by 'plotly::rename' when
loading 'ChAMP'Warning: replacing previous import 'plyr::arrange' by 'plotly::arrange' when
loading 'ChAMP'Warning: replacing previous import 'plyr::mutate' by 'plotly::mutate' when
loading 'ChAMP'Warning: replacing previous import 'plyr::is.discrete' by 'Hmisc::is.discrete'
when loading 'ChAMP'Warning: replacing previous import 'plyr::summarize' by 'Hmisc::summarize' when
loading 'ChAMP'Warning: replacing previous import 'plotly::subplot' by 'Hmisc::subplot' when
loading 'ChAMP'Warning: replacing previous import 'GenomicRanges::sort' by 'globaltest::sort'
when loading 'ChAMP'Warning: replacing previous import 'plotly::last_plot' by 'ggplot2::last_plot'
when loading 'ChAMP'Warning: replacing previous import 'globaltest::model.matrix' by
'stats::model.matrix' when loading 'ChAMP'Warning: replacing previous import 'globaltest::p.adjust' by 'stats::p.adjust'
when loading 'ChAMP'library(minfi)
library(paletteer)
library(limma)
library(BiocParallel)
library(reshape2)
library(DMRcate)
library(missMethyl)
library(ggplot2)
library(glue)
library(UpSetR)
library(dplyr)
library(patchwork)
library(tibble)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
source(here("code/utility.R"))Load data
We are using publicly available EPIC data GSE110554 generated from flow sorted blood cells. The data is normalised and filtered (bad probes, multi-mapping probes, SNP probes, sex chromosomes).
# load data
dataFile <- here("data/GSE110554-data.RData")
if(file.exists(dataFile)){
# load processed data and sample information
load(dataFile)
} else {
# get data from experiment hub, normalise, filter and save objects
readData(dataFile)
# load processed data and sample information
load(dataFile)
}Statistical analysis
Compare several sets of sorted immune cells. Consider results significant at FDR < 0.05 and delta beta ~ 10% (~ lfc = 0.5).
mVals <- getM(fltGr)
bVals <- getBeta(fltGr)design <- model.matrix(~0+targets$CellType)
colnames(design) <- levels(factor(targets$CellType))
fit <- lmFit(mVals, design)
cont.matrix <- makeContrasts(CD4vCD8=CD4T-CD8T,
MonovNeu=Mono-Neu,
BcellvNK=Bcell-NK,
levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
tfit <- eBayes(fit2, robust=TRUE, trend=TRUE)
tfit <- treat(tfit, lfc = 0.5)
pval <- 0.05
fitSum <- summary(decideTests(tfit, p.value = pval))
fitSum CD4vCD8 MonovNeu BcellvNK
Down 5072 9324 34803
NotSig 725611 712480 667559
Up 3202 12081 31523Find differentially methylated regions
Identify differentially methylated regions using the DMRcate package.
outFile <- here("data/dmrcate-results.rds")
if(!file.exists(outFile)){
dmrList <- vector("list", ncol(fitSum))
for(i in 1:ncol(fitSum)){
cpgAnn <- cpg.annotate("array", mVals, what = "M", arraytype = "EPIC",
analysis.type = "differential", design = design,
contrasts = TRUE, cont.matrix = cont.matrix,
coef = colnames(fitSum)[i])
dmrList[[i]] <- extractRanges(dmrcate(cpgAnn))
}
saveRDS(dmrList, file = outFile)
} else {
dmrList <- readRDS(outFile)
}GO analysis of DMRs
Run GO analysis on the differentially methylated regions (DMRs) identified by DMRcate for each of the contrasts.
outFile <- here("data/dmrcate-go.rds")
anno <- loadAnnotation(arrayType = "EPIC")
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
hg19Genes <- GenomicFeatures::genes(txdb)
dmrGo <- NULL
if(!file.exists(outFile)){
for(i in 1:length(dmrList)){
keep <- (abs(dmrList[[i]]$meandiff) > 0.1 & dmrList[[i]]$no.cpgs >=3)
overlaps <- findOverlaps(hg19Genes, dmrList[[i]][keep, ])
sigGenes <- hg19Genes$gene_id[from(overlaps)]
tmp <- topGO(goana(sigGenes), number = Inf)
tmp <- rownames_to_column(tmp, var = "GO")[, c("GO", "P.DE")]
tmp$method <- "goana"
tmp$contrast <- colnames(cont.matrix)[i]
dmrGo <- bind_rows(dmrGo, tmp)
tmp <- topGSA(goregion(dmrList[[i]][keep, ], anno = anno,
prior.prob = FALSE, array.type = "EPIC"),
number = Inf)
tmp <- rownames_to_column(tmp, var = "GO")[, c("GO", "P.DE")]
tmp$method <- "goregion-hgt"
tmp$contrast <- colnames(cont.matrix)[i]
dmrGo <- bind_rows(dmrGo, tmp)
tmp <- topGSA(goregion(dmrList[[i]][keep, ], anno = anno,
array.type = "EPIC"),
number = Inf)
tmp <- rownames_to_column(tmp, var = "GO")[, c("GO", "P.DE")]
tmp$method <- "goregion-gometh"
tmp$contrast <- colnames(cont.matrix)[i]
dmrGo <- bind_rows(dmrGo, tmp)
tmp <- topGSA(gometh(rownames(topTreat(tfit, coef = i, num = 5000,
p.value = pval)), anno = anno,
array.type = "EPIC"), number = Inf)
tmp <- rownames_to_column(tmp, var = "GO")[, c("GO", "P.DE")]
tmp$method <- "gometh-probe-top"
tmp$contrast <- colnames(cont.matrix)[i]
dmrGo <- bind_rows(dmrGo, tmp)
tmp <- topGSA(gometh(rownames(topTreat(tfit, coef = i, num = Inf,
p.value = pval)), anno = anno,
array.type = "EPIC"), number = Inf)
tmp <- rownames_to_column(tmp, var = "GO")[, c("GO", "P.DE")]
tmp$method <- "gometh-probe-fdr"
tmp$contrast <- colnames(cont.matrix)[i]
dmrGo <- bind_rows(dmrGo, tmp)
}
saveRDS(dmrGo, file = outFile)
} else {
dmrGo <- readRDS(outFile)
}Compare GOregion with other approaches
immuneGO <- unique(read.csv(here("data/GO-immune-system-process.txt"),
stringsAsFactors = FALSE, header = FALSE,
col.names = "GOID"))
dmrGo %>% arrange(contrast, method, P.DE) %>%
group_by(contrast, method) %>%
mutate(csum = cumsum(GO %in% immuneGO$GOID)) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> dat
p <- ggplot(dat, aes(x = rank, y = csum, colour = method)) +
geom_line() +
facet_wrap(vars(contrast), ncol=3) +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Method", x = "Rank", y = "Cumulative no. immune sets")
p
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
immuneGO <- readRDS(here("data/RNAseq-GO.rds"))
immuneGO %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% arrange(contrast, method, P.DE) %>%
group_by(contrast, method) %>%
mutate(csum = cumsum(GO %in% topSets$ID[topSets$contrast %in% contrast])) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = method)) +
geom_line() +
facet_wrap(vars(contrast), ncol=3) +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Method", x = "Rank",
y = glue("Cumulative no. RNAseq sets")) +
theme(legend.position = "bottom")
p
Examine what the top 10 ranked gene sets are and how many genes they contain, for each method and comparison.
terms <- missMethyl:::.getGO()$idTable
nGenes <- rownames_to_column(data.frame(n = sapply(missMethyl:::.getGO()$idList,
length)),
var = "ID")
dat %>% arrange(contrast, method, P.DE) %>%
group_by(contrast, method) %>%
mutate(FDR = p.adjust(P.DE, method = "BH")) %>%
filter(rank <= 10) %>%
inner_join(terms, by = c("GO" = "GOID")) %>%
inner_join(nGenes, by = c("GO" = "ID")) -> sub
p <- vector("list", length(unique(sub$contrast)) * length(unique(sub$method)))
i = 1
for(cont in unique(sub$contrast)){
c = 1
for(meth in unique(sub$method)){
tmp <- sub %>% filter(contrast == cont & method == meth) %>%
mutate(rank = factor(rank),
rank = factor(rank, levels = rev(levels(rank))))
p[[i]] <- ggplot(tmp, aes(x = -log10(FDR), y = rank)) +
geom_point(aes(size = n), alpha = 0.5,
colour = scales::hue_pal()(length(unique(sub$method)))[c]) +
scale_y_discrete(labels = rev(tmp$TERM)) +
labs(y = "", size = "No. genes", title = meth) +
theme(axis.text.y = element_text(size = 6),
plot.title = element_text(size = 8),
legend.position = "right",
legend.key.size = unit(0.25, "cm"),
legend.text = element_text(size = 6),
legend.title = element_text(size = 8),
axis.text.x = element_text(size = 6),
axis.title.x = element_text(size = 8)) +
coord_cartesian(xlim = c(-log10(0.99), -log10(10^-100))) +
geom_vline(xintercept = -log10(0.05), linetype = "dashed")
i = i + 1
c = c + 1
}
}
(p[[1]] / p[[2]] / p[[3]] / p[[4]] / p[[5]]) +
plot_annotation(title = unique(sub$contrast)[1],
theme = theme(plot.title = element_text(size = 10))) 
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
(p[[6]] / p[[7]] / p[[8]] / p[[9]] / p[[10]]) +
plot_annotation(title = unique(sub$contrast)[2],
theme = theme(plot.title = element_text(size = 10))) 
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
(p[[11]] / p[[12]] / p[[13]] / p[[14]] / p[[15]]) +
plot_annotation(title = unique(sub$contrast)[3],
theme = theme(plot.title = element_text(size = 10))) 
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
Compare characteristics of region-wise and probe-wise results
cpgs <- GRanges(seqnames = anno$chr,
ranges = IRanges(start = anno$pos,
end = anno$pos),
strand = anno$strand,
name = anno$Name)
dat <- NULL
for(i in 1:ncol(cont.matrix)){
overlaps <- findOverlaps(cpgs, dmrList[[i]])
tmp <- data.frame(cpgs = cpgs$name[from(overlaps)],
method = "dmrcate",
contrast = colnames(cont.matrix)[i],
stringsAsFactors = FALSE)
dat <- bind_rows(dat, tmp)
tmp <- data.frame(cpgs = rownames(topTreat(tfit, coef = i, num = 5000)),
method = "probe-top",
contrast = colnames(cont.matrix)[i],
stringsAsFactors = FALSE)
dat <- bind_rows(dat, tmp)
tmp <- data.frame(cpgs = rownames(topTreat(tfit, coef = i, num = Inf,
p.value = pval)),
method = "probe-fdr",
contrast = colnames(cont.matrix)[i],
stringsAsFactors = FALSE)
dat <- bind_rows(dat, tmp)
}
dat %>% group_by(contrast, method) %>% tally() -> sub
ggplot(sub, aes(x = method, y = n, fill = method)) +
geom_bar(stat = "identity", show.legend = FALSE) +
facet_wrap(vars(contrast)) +
labs(fill = "Method", y = "No. significant CpGs", x = "Method") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
flatAnn <- loadFlatAnnotation(anno)
dat %>% group_by(contrast, method) %>%
inner_join(flatAnn, by = c("cpgs" = "cpg")) %>%
group_by(contrast, method) %>%
dplyr::select(group_cols(), entrezid) %>%
distinct() %>%
tally() -> sub
ggplot(sub, aes(x = method, y = n, fill = method)) +
geom_bar(stat = "identity", show.legend = FALSE) +
facet_wrap(vars(contrast)) +
labs(fill = "Method", y = "No. genes with sig. CpGs", x = "Method") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
dat %>% group_by(contrast, method) %>%
left_join(flatAnn, by = c("cpgs" = "cpg")) %>%
group_by(contrast, method) %>%
dplyr::select(group_cols(), entrezid, cpgs) %>%
summarise(prop = sum(!is.na(entrezid[!duplicated(cpgs)]))/
length(unique(cpgs))) -> sub
ggplot(sub, aes(x = method, y = prop, fill = method)) +
geom_bar(stat = "identity", show.legend = FALSE) +
facet_wrap(vars(contrast)) +
labs(fill = "Method", y = "Prop. sig. CpGs mapped to genes", x = "Method") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
dat %>% group_by(contrast, method) %>%
left_join(flatAnn, by = c("cpgs" = "cpg")) %>%
group_by(contrast, method) %>%
dplyr::select(group_cols(), group, cpgs) %>%
group_by(contrast, method, group) %>%
tally() -> sub
ggplot(sub, aes(x = group, y = n, fill = method)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(vars(contrast), nrow = 3, ncol = 1, scales = "free_y") +
labs(fill = "Method", y = "No. sig. CpGs mapped to genomic features",
x = "Feature") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
CpG overlap between region-wise and probe-wise approaches
Compare the CpGs covered by the different approaches, for the three contrasts.
p <- vector("list", ncol(cont.matrix))
for(i in 1:ncol(cont.matrix)){
dat %>% filter(contrast == colnames(cont.matrix)[i]) -> tmp
tmp <- split(tmp$cpgs, f = tmp$method)
p[[i]] <- upset(fromList(tmp), order.by = "freq", keep.order = TRUE,
sets = names(tmp))
}
p[[1]]
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
p[[2]]
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
p[[3]]
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
Gene overlap between region-wise and probe-wise approaches
Compare the genes covered by the different approaches, for the three contrasts.
p <- vector("list", ncol(cont.matrix))
for(i in 1:ncol(cont.matrix)){
dat %>% filter(contrast == colnames(cont.matrix)[i]) %>%
left_join(flatAnn, by = c("cpgs" = "cpg")) %>%
dplyr::select(method, entrezid) %>%
distinct() -> tmp
tmp <- split(tmp$entrezid, f = tmp$method)
p[[i]] <- upset(fromList(tmp), order.by = "freq", keep.order = TRUE,
sets = names(tmp))
}
p[[1]]
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
p[[2]]
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
p[[3]]
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
Effect of DMR cut offs i.e. num probes in region and absolute delta beta
outFile <- here("data/dmrcate-params.rds")
dmrParams <- NULL
meanDiffs <- seq(0, 0.2, by = 0.1)
noCpgs <- 2:4
if(!file.exists(outFile)){
for(i in 1:length(dmrList)){
for(j in meanDiffs){
for(k in noCpgs){
keep <- (abs(dmrList[[i]]$meandiff) > j &
dmrList[[i]]$no.cpgs >= k)
tmp <- topGSA(goregion(dmrList[[i]][keep, ], anno = anno,
array.type = "EPIC"),
number = Inf)
tmp <- rownames_to_column(tmp, var = "GO")[, c("GO", "P.DE")]
tmp$params <- glue("|Beta| = {j}; No. CpGs = {k}")
tmp$contrast <- colnames(cont.matrix)[i]
dmrParams <- bind_rows(dmrParams, tmp)
}
}
}
saveRDS(dmrParams, file = outFile)
} else {
dmrParams <- readRDS(outFile)
}Examine effect of changing DMr parameter cut offs on gene set rankings of GO categories in “immune system process”.
immuneGO <- unique(read.csv(here("data/GO-immune-system-process.txt"),
stringsAsFactors = FALSE, header = FALSE,
col.names = "GOID"))
dmrParams %>% arrange(contrast, params, P.DE) %>%
group_by(contrast, params) %>%
mutate(csum = cumsum(GO %in% immuneGO$GOID)) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> dat
p <- ggplot(dat, aes(x = rank, y = csum, colour = params)) +
geom_line() +
facet_wrap(vars(contrast), ncol=3) +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Parameters", x = "Rank", y = "Cumulative no. immune sets")
p
| Version | Author | Date |
|---|---|---|
| d930b45 | JovMaksimovic | 2020-04-27 |
Examine effect of changing DMR parameter cut offs on gene set rankings on GO categories derived from RNAseq analysis.
immuneGO <- readRDS(here("data/RNAseq-GO.rds"))
immuneGO %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dmrParams %>% arrange(contrast, params, P.DE) %>%
group_by(contrast, params) %>%
mutate(csum = cumsum(GO %in% topSets$ID[topSets$contrast %in% contrast])) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = params)) +
geom_line() +
facet_wrap(vars(contrast), ncol=3) +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Parameters", x = "Rank",
y = glue("Cumulative no. RNAseq sets"))
p
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[2] GenomicFeatures_1.38.2
[3] tibble_3.0.1
[4] patchwork_1.0.0
[5] dplyr_0.8.5
[6] UpSetR_1.4.0
[7] glue_1.4.0
[8] ggplot2_3.3.0
[9] missMethyl_1.20.4
[10] reshape2_1.4.4
[11] paletteer_1.1.0
[12] ChAMP_2.16.2
[13] DT_0.13
[14] IlluminaHumanMethylationEPICmanifest_0.3.0
[15] Illumina450ProbeVariants.db_1.22.0
[16] DMRcate_2.0.7
[17] FEM_3.14.0
[18] graph_1.64.0
[19] org.Hs.eg.db_3.10.0
[20] impute_1.60.0
[21] igraph_1.2.5
[22] corrplot_0.84
[23] marray_1.64.0
[24] limma_3.42.2
[25] Matrix_1.2-18
[26] AnnotationDbi_1.48.0
[27] ChAMPdata_2.18.0
[28] minfi_1.32.0
[29] bumphunter_1.28.0
[30] locfit_1.5-9.4
[31] iterators_1.0.12
[32] foreach_1.5.0
[33] Biostrings_2.54.0
[34] XVector_0.26.0
[35] SummarizedExperiment_1.16.1
[36] DelayedArray_0.12.3
[37] BiocParallel_1.20.1
[38] matrixStats_0.56.0
[39] Biobase_2.46.0
[40] GenomicRanges_1.38.0
[41] GenomeInfoDb_1.22.1
[42] IRanges_2.20.2
[43] S4Vectors_0.24.4
[44] BiocGenerics_0.32.0
[45] here_0.1
[46] workflowr_1.6.1
loaded via a namespace (and not attached):
[1] rappdirs_0.3.1
[2] rtracklayer_1.46.0
[3] R.methodsS3_1.8.0
[4] wateRmelon_1.30.0
[5] tidyr_1.0.2
[6] acepack_1.4.1
[7] bit64_0.9-7
[8] knitr_1.28
[9] R.utils_2.9.2
[10] data.table_1.12.8
[11] rpart_4.1-15
[12] doParallel_1.0.15
[13] RCurl_1.98-1.2
[14] GEOquery_2.54.1
[15] AnnotationFilter_1.10.0
[16] preprocessCore_1.48.0
[17] RSQLite_2.2.0
[18] combinat_0.0-8
[19] bit_1.1-15.2
[20] xml2_1.3.2
[21] httpuv_1.5.2
[22] assertthat_0.2.1
[23] IlluminaHumanMethylation450kmanifest_0.4.0
[24] viridis_0.5.1
[25] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
[26] isva_1.9
[27] xfun_0.13
[28] hms_0.5.3
[29] evaluate_0.14
[30] DNAcopy_1.60.0
[31] promises_1.1.0
[32] scrime_1.3.5
[33] progress_1.2.2
[34] dendextend_1.13.4
[35] dbplyr_1.4.3
[36] DBI_1.1.0
[37] htmlwidgets_1.5.1
[38] reshape_0.8.8
[39] purrr_0.3.4
[40] ROC_1.62.0
[41] ellipsis_0.3.0
[42] backports_1.1.6
[43] permute_0.9-5
[44] annotate_1.64.0
[45] biomaRt_2.42.1
[46] vctrs_0.2.4
[47] ensembldb_2.10.2
[48] withr_2.2.0
[49] globaltest_5.40.0
[50] Gviz_1.30.3
[51] BSgenome_1.54.0
[52] checkmate_2.0.0
[53] GenomicAlignments_1.22.1
[54] prettyunits_1.1.1
[55] mclust_5.4.6
[56] cluster_2.1.0
[57] RPMM_1.25
[58] ExperimentHub_1.12.0
[59] lazyeval_0.2.2
[60] crayon_1.3.4
[61] genefilter_1.68.0
[62] labeling_0.3
[63] edgeR_3.28.1
[64] pkgconfig_2.0.3
[65] palr_0.2.0
[66] nlme_3.1-147
[67] ProtGenerics_1.18.0
[68] pals_1.6
[69] nnet_7.3-14
[70] rlang_0.4.5
[71] lifecycle_0.2.0
[72] nleqslv_3.3.2
[73] affyio_1.56.0
[74] BiocFileCache_1.10.2
[75] AnnotationHub_2.18.0
[76] dichromat_2.0-0
[77] rprojroot_1.3-2
[78] rngtools_1.5
[79] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
[80] base64_2.0
[81] Rhdf5lib_1.8.0
[82] base64enc_0.1-3
[83] geneLenDataBase_1.22.0
[84] whisker_0.4
[85] viridisLite_0.3.0
[86] png_0.1-7
[87] oompaBase_3.2.9
[88] bitops_1.0-6
[89] R.oo_1.23.0
[90] KernSmooth_2.23-17
[91] blob_1.2.1
[92] DelayedMatrixStats_1.8.0
[93] doRNG_1.8.2
[94] qvalue_2.18.0
[95] stringr_1.4.0
[96] nor1mix_1.3-0
[97] readr_1.3.1
[98] jpeg_0.1-8.1
[99] scales_1.1.0
[100] memoise_1.1.0
[101] magrittr_1.5
[102] plyr_1.8.6
[103] zlibbioc_1.32.0
[104] compiler_3.6.3
[105] RColorBrewer_1.1-2
[106] illuminaio_0.28.0
[107] clue_0.3-57
[108] JADE_2.0-3
[109] Rsamtools_2.2.3
[110] DSS_2.34.0
[111] affy_1.64.0
[112] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
[113] htmlTable_1.13.3
[114] Formula_1.2-3
[115] MASS_7.3-51.6
[116] mgcv_1.8-31
[117] tidyselect_1.0.0
[118] stringi_1.4.6
[119] yaml_2.2.1
[120] askpass_1.1
[121] latticeExtra_0.6-29
[122] grid_3.6.3
[123] VariantAnnotation_1.32.0
[124] tools_3.6.3
[125] ruv_0.9.7.1
[126] rstudioapi_0.11
[127] foreign_0.8-76
[128] git2r_0.26.1
[129] bsseq_1.22.0
[130] gridExtra_2.3
[131] farver_2.0.3
[132] digest_0.6.25
[133] BiocManager_1.30.10
[134] shiny_1.4.0.2
[135] quadprog_1.5-8
[136] Rcpp_1.0.4.6
[137] siggenes_1.60.0
[138] BiocVersion_3.10.1
[139] later_1.0.0
[140] httr_1.4.1
[141] biovizBase_1.34.1
[142] lumi_2.38.0
[143] colorspace_1.4-1
[144] XML_3.99-0.3
[145] fs_1.4.1
[146] splines_3.6.3
[147] statmod_1.4.34
[148] rematch2_2.1.1
[149] kpmt_0.1.0
[150] multtest_2.42.0
[151] mapproj_1.2.7
[152] shinythemes_1.1.2
[153] plotly_4.9.2.1
[154] jcolors_0.0.4
[155] xtable_1.8-4
[156] jsonlite_1.6.1
[157] scico_1.1.0
[158] R6_2.4.1
[159] Hmisc_4.4-0
[160] pillar_1.4.3
[161] htmltools_0.4.0
[162] mime_0.9
[163] fastmap_1.0.1
[164] interactiveDisplayBase_1.24.0
[165] beanplot_1.2
[166] codetools_0.2-16
[167] maps_3.3.0
[168] lattice_0.20-41
[169] sva_3.34.0
[170] curl_4.3
[171] BiasedUrn_1.07
[172] gtools_3.8.2
[173] GO.db_3.10.0
[174] openssl_1.4.1
[175] survival_3.1-12
[176] rmarkdown_2.1
[177] methylumi_2.32.0
[178] fastICA_1.2-2
[179] munsell_0.5.0
[180] rhdf5_2.30.1
[181] GenomeInfoDbData_1.2.2
[182] goseq_1.38.0
[183] HDF5Array_1.14.4
[184] gtable_0.3.0