Gene set testing for Illumina HumanMethylation Arrays
Evaluating the performance of different methods
Jovana Maksimovic, Alicia Oshlack and Belinda Phipson
May 19, 2020
Last updated: 2020-05-19
Checks: 7 0
Knit directory: methyl-geneset-testing/
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|---|---|---|---|---|
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| Rmd | 25e58bc | JovMaksimovic | 2020-05-11 | Added BH to pdjust calls, added methylation vs expression plot. |
| Rmd | cb5e0b1 | JovMaksimovic | 2020-05-08 | Added delta M vs. log FC plot for methylation vs. RNAseq data for each contrast. |
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| Rmd | 3ae5062 | Jovana Maksimovic | 2020-04-21 | Updated code to reverse bubble plot y-axis |
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| Rmd | 2bcb4c9 | JovMaksimovic | 2020-03-23 | New script and modifications for methylGSA analysis. |
| Rmd | 250d8ae | JovMaksimovic | 2020-03-16 | Comitting local changes. |
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| Rmd | d7cd66e | Jovana Maksimovic | 2020-03-02 | Initial Commit |
library(here)
library(minfi)
library(paletteer)
library(limma)
library(BiocParallel)
library(reshape2)
library(gridExtra)
library(missMethyl)
library(ggplot2)
library(glue)
library(tidyverse)
library(rbin)
library(patchwork)
library(ChAMPdata)
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)
}QC plots
# plot mean detection p-values across all samples
dat <- tibble::tibble(mean = colMeans(detP), cellType = targets$CellType)
ggplot(dat, aes(y = mean, x = cellType, fill = cellType)) +
geom_bar(stat = "identity") +
labs(fill = "Cell Type")
# plot normalised beta value distribution
bVals <- getBeta(normGr)
dat <- data.frame(reshape2::melt(bVals))
colnames(dat) <- c("cpg", "sample", "bVal")
dat <- dplyr::bind_cols(dat, cellType = rep(targets$CellType,
each = nrow(bVals)))
ggplot(dat, aes(x = bVal, colour = cellType)) +
geom_density() +
labs(colour = "Cell Type")
# MDS plots to look at largest sources of variation
p <- plotMDS(getM(fltGr), top=1000, gene.selection="common", plot = FALSE)
dat <- tibble::tibble(x = p$x, y = p$y, cellType = targets$CellType)
ggplot(dat, aes(x = x, y = y, colour = cellType)) +
geom_point() +
labs(colour = "Cell Type")
| Version | Author | Date |
|---|---|---|
| da4f010 | JovMaksimovic | 2020-05-08 |
MDS plots using only the CpGs that map to genes.
ann <- loadAnnotation("EPIC")
flatAnn <- missMethyl:::.getFlatAnnotation("EPIC", anno = ann)
geneM <- getM(fltGr[rownames(fltGr) %in% flatAnn$cpg,])
p <- plotMDS(geneM, top=1000, gene.selection="common", plot = FALSE)
dat <- tibble::tibble(x = p$x, y = p$y, cellType = targets$CellType)
ggplot(dat, aes(x = x, y = y, colour = cellType)) +
geom_point() +
labs(colour = "Cell Type")
Statistical analysis
Compare several sets of sorted immune cells. Consider results significant at FDR < 0.05 and delta beta ~ 10% (~ lfc = 0.5).
mVals <- getM(fltGr)
bVals <- getBeta(fltGr)design <- model.matrix(~0+targets$CellType)
colnames(design) <- levels(factor(targets$CellType))
fit <- lmFit(mVals, design)
cont.matrix <- makeContrasts(CD4vCD8=CD4T-CD8T,
#BcellvMono=Bcell-Mono,
MonovNeu=Mono-Neu,
BcellvNK=Bcell-NK,
#NeuvNK=Neu-NK,
#MonovNK=Mono-NK,
levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
tfit <- eBayes(fit2, robust=TRUE, trend=TRUE)
tfit <- treat(tfit, lfc = 0.5)
pval <- 0.05
fitSum <- summary(decideTests(tfit, p.value = pval))
fitSum CD4vCD8 MonovNeu BcellvNK
Down 5072 9324 34803
NotSig 725611 712480 667559
Up 3202 12081 31523rfit <- readRDS(here("data/rnaseq-fit.rds"))
rdat <- rownames_to_column(data.frame(rfit$coefficients), var = "gene_id")
rdat %>% inner_join(data.frame(rfit$genes)) %>%
mutate(entrezid = as.character(entrezid)) %>%
dplyr::select(-length) -> rdatJoining, by = "gene_id"rdat <- reshape2::melt(rdat, value.name = "coefficient")Using gene_id, symbol, entrezid as id variablesreshape2::melt(rfit$p.value, value.name = "p.value") %>%
group_by(Contrasts) %>%
mutate(FDR = p.adjust(p.value, method = "BH")) -> rfdr
rdat %>% inner_join(rfdr,
by = c("gene_id" = "Var1", "variable" = "Contrasts")) -> rdatWarning: Column `gene_id`/`Var1` joining character vector and factor, coercing
into character vectormdat <- rownames_to_column(data.frame(tfit$coefficients), var = "cpg")
mdat %>% inner_join(flatAnn) %>%
dplyr::select(-symbol, -alias) -> mdatJoining, by = "cpg"mdat <- reshape2::melt(mdat, value.name = "coefficient") -> mdatUsing cpg, group, entrezid as id variablesmdat %>% dplyr::select(-cpg) %>%
mutate(promoter = ifelse(group %in% c("TSS1500", "TSS200", "1stExon"),
"prom", "other")) %>%
group_by(variable, entrezid, promoter) %>%
#summarise(coef = coefficient[which.max(abs(coefficient))]) -> mdat
summarise(coef = median(coefficient)) %>%
filter(promoter == "prom") -> mdat
mdat %>% inner_join(rdat, by = c("entrezid", "variable")) %>%
mutate(colour = ifelse(FDR < 0.05, "Sig.", "Not sig.")) -> dat
ggplot(dat, aes(x = coef, y = coefficient, colour = colour)) +
geom_point(data = dat[dat$colour == 'Not sig.',], alpha = 0.25,
size = 1) +
geom_point(data = dat[dat$colour == 'Sig.',], size = 0.75, alpha = 0.5) +
facet_wrap(vars(variable), ncol = 2, scales = "free") +
labs(x = "Median logFC for CpGs in gene promoter",
y = "Gene expression Log FC", color = "Diff. Gene Exp.")
| Version | Author | Date |
|---|---|---|
| afc650b | JovMaksimovic | 2020-05-11 |
Examine only the independent contrasts.
dat <- melt(fitSum[rownames(fitSum) != "NotSig", ])
colnames(dat) <- c("dir","comp","num")
ggplot(dat, aes(x = comp, y = num, fill = dir)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x = "Comparison", y = "No. DM CpGs (FDR < 0.05)", fill = "Direction")
Compare performance of different gene set testing methods
Compare the gene set testing methods available for methylation arrays; hypergeometric test (HGT), gometh (best), methylRRA (GLM), methylRRA (ORA) and methylRRA (GSEA). As methylRRA does not work well with sets that only contain very few genes or very large sets, we will only test sets with at least 5 genes and maximum of 5000 genes.
Save analysis results
minsize <- 5
maxsize <- 5000
outFile <- here("data/blood.contrasts.rds")
if(!file.exists(outFile)){
obj <- NULL
obj$tfit <- tfit
obj$maxsize <- maxsize
obj$minsize <- minsize
obj$mVals <- mVals
obj$targets <- targets
saveRDS(obj, file = outFile)
} Load output for all methods
inFiles <- list.files(here("output/compare-methods"), pattern = "rds",
full.names = TRUE)
res <- lapply(inFiles, function(file){
readRDS(file)
})
dat <- as_tibble(dplyr::bind_rows(res))sub <- dat %>% filter(method %in% c("mmethyl.hgt", "mmethyl.gometh"))
ggplot(sub, aes(x = pvalue, colour = sub)) +
geom_density() +
facet_grid(cols = vars(contrast), rows = vars(method)) +
labs(colour = "Sig. CpG Selection", x = "P-value", y = "Density") +
scale_color_hue(labels = c("Top 5000", "Top 10000", "FDR < 0.01",
"FDR < 0.05")) +
theme(legend.position = "bottom", legend.text = element_text(size=6))
Test GO categories
cpgEgGo <- cpgsEgGoFreqs(flatAnn)cpgEgGo %>%
group_by(GO) %>%
summarise(med = median(Freq)) -> medCpgEgGo
dat %>% filter(set == "GO") %>%
filter(sub %in% c("n","c1")) %>%
inner_join(medCpgEgGo, by = c("ID" = "GO")) -> sub
bins <- rbin_quantiles(sub, ID, med, bins = 12)
sub$bin <- as.factor(findInterval(sub$med, bins$upper_cut))
binLabs <- paste0("<", bins$upper_cut)
names(binLabs) <- levels(sub$bin)
binLabs[length(binLabs)] <- gsub("<", "\u2265", binLabs[length(binLabs) - 1])
sub %>% group_by(contrast, method, bin) %>%
summarise(prop = sum(pvalue < 0.05)/n()) -> pdat
p <- ggplot(pdat, aes(x = as.numeric(bin), y = prop, color = method)) +
geom_line() +
facet_wrap(vars(contrast), ncol = 3) +
scale_x_continuous(breaks = as.numeric(levels(pdat$bin)), labels = binLabs) +
theme(axis.text.x = element_text(angle=45, hjust = 1, vjust = 1,
size = 7),
legend.position = "bottom") +
labs(x = "Med. No. CpGs per Gene per GO Cat. (binned)",
y = "Prop. GO Cat. with p-value < 0.05")
p
As we are comparing immune cells, we expect GO categories related to the immune system and its processes to be highly ranked. To evalue this, we downloaded all of the child terms for the GO category “immune system process” (GO:002376) from AminGO 2; http://amigo.geneontology.org/amigo/term/GO:0002376.
immuneGO <- unique(read.csv(here("data/GO-immune-system-process.txt"),
stringsAsFactors = FALSE, header = FALSE,
col.names = "GOID"))
dat %>% filter(set == "GO") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(csum = cumsum(ID %in% immuneGO$GOID)) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = method)) +
geom_line() +
facet_wrap(vars(contrast), ncol=3) +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Method", x = "Rank", y = "Cumulative no. immune sets") +
theme(legend.position = "bottom")
p
Examine results when top ranked 100 GO terms from RNA-seq analysis of the same cell type comparisons is used as “truth”.
immuneGO <- readRDS(here("data/RNAseq-GO.rds"))
immuneGO %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% filter(set == "GO") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(csum = cumsum(ID %in% topSets$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()$idList,
length)),
var = "ID")
dat %>% filter(set == "GO") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(FDR = p.adjust(pvalue, method = "BH")) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 10) %>%
inner_join(terms, by = c("ID" = "GOID")) %>%
inner_join(nGenes) -> subJoining, by = "ID"p <- vector("list", length(unique(sub$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))) 
(p[[6]] / p[[7]] / p[[8]] / p[[9]] / p[[10]]) +
plot_annotation(title = unique(sub$contrast)[2],
theme = theme(plot.title = element_text(size = 10))) 
(p[[11]] / p[[12]] / p[[13]] / p[[14]] / p[[15]]) +
plot_annotation(title = unique(sub$contrast)[3],
theme = theme(plot.title = element_text(size = 10))) 
P-value histograms for the different methods for all contrasts on GO categories.
dat %>% filter(set == "GO") %>%
filter(sub %in% c("n","c1")) -> sub
ggplot(sub, aes(pvalue, fill = method)) +
geom_histogram(binwidth = 0.025) +
facet_grid(cols = vars(contrast), rows = vars(method)) +
theme(legend.position = "bottom") +
labs(x = "P-value", y = "Frequency", fill = "Method")
Compare gometh results using different DM CpG significance cutoffs
immuneGO <- unique(read.csv(here("data/GO-immune-system-process.txt"),
stringsAsFactors = FALSE, header = FALSE,
col.names = "GOID"))
dat %>% filter(set == "GO") %>%
filter(grepl("mmethyl", method)) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method, sub) %>%
mutate(csum = cumsum(ID %in% immuneGO$GOID)) %>%
mutate(rank = 1:n()) %>%
mutate(cut = ifelse(sub == "c1", "Top 5000",
ifelse(sub == "c2", "Top 10000",
ifelse(sub == "p1", "FDR < 0.01", "FDR < 0.05")))) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = cut)) +
geom_line() +
facet_wrap(vars(contrast, method), ncol=2, nrow = 3, scales = "free") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Sig. select", x = "Rank", y = "Cumulative no. immune sets") +
theme(legend.position = "bottom")
p
immuneGO <- readRDS(here("data/RNAseq-GO.rds"))
immuneGO %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% filter(set == "GO") %>%
filter(grepl("mmethyl", method)) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method, sub) %>%
mutate(csum = cumsum(ID %in% topSets$ID[topSets$contrast %in% contrast])) %>%
mutate(rank = 1:n()) %>%
mutate(cut = ifelse(sub == "c1", "Top 5000",
ifelse(sub == "c2", "Top 10000",
ifelse(sub == "p1", "FDR < 0.01", "FDR < 0.05")))) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = cut)) +
geom_line() +
facet_wrap(vars(contrast, method), ncol=2, nrow = 3, scales = "free") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Sig. select", x = "Rank",
y = glue("Cumulative no. RNAseq sets")) +
theme(legend.position = "bottom")
p
Test KEGG pathways
Now test KEGG pathways with at least 5 genes and 5000 at most.
Again, as we are comparing immune cells we expecte pathways from the following categories to be highly ranked: Immune system, Immune disease, Signal transduction, Signaling molecules and interaction; https://www.genome.jp/kegg/pathway.html.
immuneKEGG <- read.csv(here("data/kegg-immune-related-pathways.csv"),
stringsAsFactors = FALSE, header = FALSE,
col.names = c("ID","pathway"))
immuneKEGG$PID <- paste0("path:hsa0",immuneKEGG$ID)
dat %>% filter(set == "KEGG") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(csum = cumsum(ID %in% immuneKEGG$PID)) %>%
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 = "Cumulative no. immune sets") +
theme(legend.position = "bottom")
p
Examine results when top ranked 100 KEGG pathways from RNA-seq analysis of the same cell type comparisons is used as “truth”.
immuneKEGG <- readRDS(here("data/RNAseq-KEGG.rds"))
immuneKEGG %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% filter(set == "KEGG") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(csum = cumsum(ID %in% topSets$PID[topSets$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, scales = "free_y") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Method", x = "Rank", y = "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:::.getKEGG()$idTable
nGenes <- rownames_to_column(data.frame(n = sapply(missMethyl:::.getKEGG()$idList,
length)),
var = "ID")
dat %>% filter(set == "KEGG") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(FDR = p.adjust(pvalue, method = "BH")) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 10) %>%
inner_join(terms, by = c("ID" = "PathwayID")) %>%
inner_join(nGenes) -> subJoining, by = "ID"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$method)))[c]) +
scale_y_discrete(labels = rev(tmp$Description)) +
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^-15))) +
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$contrast)[1],
theme = theme(plot.title = element_text(size = 10))) 
(p[[6]] / p[[7]] / p[[8]] / p[[9]] / p[[10]]) +
plot_annotation(title = unique(sub$contrast)[2],
theme = theme(plot.title = element_text(size = 10))) 
(p[[11]] / p[[12]] / p[[13]] / p[[14]] / p[[15]]) +
plot_annotation(title = unique(sub$contrast)[3],
theme = theme(plot.title = element_text(size = 10))) 
P-value histograms for the different methods for all contrasts on KEGG pathways.
dat %>% filter(set == "KEGG") %>%
filter(sub %in% c("n","c1")) -> sub
ggplot(sub, aes(pvalue, fill = method)) +
geom_histogram(binwidth = 0.025) +
facet_grid(cols = vars(contrast), rows = vars(method)) +
theme(legend.position = "bottom") +
labs(x = "P-value", y = "Frequency", fill = "Method")
Compare gometh results using different DM CpG significance cutoffs
immuneKEGG <- read.csv(here("data/kegg-immune-related-pathways.csv"),
stringsAsFactors = FALSE, header = FALSE,
col.names = c("ID","pathway"))
immuneKEGG$PID <- paste0("path:hsa0",immuneKEGG$ID)
dat %>% filter(set == "KEGG") %>%
filter(grepl("mmethyl", method)) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method, sub) %>%
mutate(csum = cumsum(ID %in% immuneKEGG$PID)) %>%
mutate(rank = 1:n()) %>%
mutate(cut = ifelse(sub == "c1", "Top 5000",
ifelse(sub == "c2", "Top 10000",
ifelse(sub == "p1", "FDR < 0.01", "FDR < 0.05")))) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = cut)) +
geom_line() +
facet_wrap(vars(contrast, method), ncol=2, nrow = 3, scales = "free") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Sig. select", x = "Rank", y = "Cumulative no. immune sets") +
theme(legend.position = "bottom")
p
| Version | Author | Date |
|---|---|---|
| afc650b | JovMaksimovic | 2020-05-11 |
immuneKEGG <- readRDS(here("data/RNAseq-KEGG.rds"))
immuneKEGG %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% filter(set == "KEGG") %>%
filter(grepl("mmethyl", method)) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method, sub) %>%
mutate(csum = cumsum(ID %in% topSets$PID[topSets$contrast %in% contrast])) %>%
mutate(rank = 1:n()) %>%
mutate(cut = ifelse(sub == "c1", "Top 5000",
ifelse(sub == "c2", "Top 10000",
ifelse(sub == "p1", "FDR < 0.01", "FDR < 0.05")))) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = cut)) +
geom_line() +
facet_wrap(vars(contrast, method), ncol=2, nrow = 3, scales = "free") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Sig. select", x = "Rank",
y = glue("Cumulative no. RNAseq sets")) +
theme(legend.position = "bottom")
p
Test BROAD gene sets
As the ebGSEA method from the ChAMP package only allows testing of their in-built database of Broad Institute gene sets, we will compare to other methods by testing these same gene sets. We are using the top 100 ranked gene sets as identified by camera analysis of the corresponding RNAseq data as “truth”.
immuneBROAD <- readRDS(here("data/RNAseq-BROAD.rds"))
immuneBROAD %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% filter(set == "BROAD") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(csum = cumsum(ID %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, scales = "free_y") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Method", x = "Rank", y = "Cumulative no. RNAseq sets") +
theme(legend.position = "bottom")
p
Using the top 100 ranked gene sets as identified by Belinda’s gsaseq analysis of the corresponding RNAseq data as “truth”.
immuneBROAD <- readRDS(here("data/RNAseq-BROAD-GSA.rds"))
immuneBROAD %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% filter(set == "BROAD") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(csum = cumsum(ID %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, scales = "free_y") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Method", x = "Rank", y = "Cumulative no. RNAseq sets") +
theme(legend.position = "bottom")
p
data(PathwayList)
nGenes <- rownames_to_column(data.frame(n = sapply(PathwayList,
length)),
var = "ID")
dat %>% filter(set == "BROAD") %>%
filter(sub %in% c("n","c1")) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method) %>%
mutate(FDR = p.adjust(pvalue, method = "BH")) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 10) %>%
inner_join(nGenes) -> subJoining, by = "ID"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$method)))[c]) +
scale_y_discrete(labels = rev(tmp$ID)) +
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]] / p[[6]] / p[[7]]) +
plot_annotation(title = unique(sub$contrast)[1],
theme = theme(plot.title = element_text(size = 10))) 
(p[[8]] / p[[9]] / p[[10]] / p[[11]] / p[[12]] / p[[13]] / p[[14]]) +
plot_annotation(title = unique(sub$contrast)[2],
theme = theme(plot.title = element_text(size = 10))) 
(p[[15]] / p[[16]] / p[[17]] / p[[18]] / p[[19]] / p[[20]] / p[[21]]) +
plot_annotation(title = unique(sub$contrast)[3],
theme = theme(plot.title = element_text(size = 10))) 
P-value histograms for the different methods for all contrasts on BROAD gene sets.
dat %>% filter(set == "BROAD") %>%
filter(sub %in% c("n","c1")) -> sub
ggplot(sub, aes(pvalue, fill = method)) +
geom_histogram(binwidth = 0.025) +
facet_grid(cols = vars(contrast), rows = vars(method)) +
theme(legend.position = "bottom") +
labs(x = "P-value", y = "Frequency", fill = "Method")
Compare gometh results using different DM CpG significance cutoffs
immuneBROAD <- readRDS(here("data/RNAseq-BROAD.rds"))
immuneBROAD %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% filter(set == "BROAD") %>%
filter(grepl("mmethyl", method)) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method, sub) %>%
mutate(csum = cumsum(ID %in% topSets$ID)) %>%
mutate(rank = 1:n()) %>%
mutate(cut = ifelse(sub == "c1", "Top 5000",
ifelse(sub == "c2", "Top 10000",
ifelse(sub == "p1", "FDR < 0.01", "FDR < 0.05")))) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = cut)) +
geom_line() +
facet_wrap(vars(contrast, method), ncol=2, nrow = 3, scales = "free") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Sig. select", x = "Rank", y = "Cumulative no. immune sets") +
theme(legend.position = "bottom")
p
immuneBROAD <- readRDS(here("data/RNAseq-BROAD-GSA.rds"))
immuneBROAD %>% group_by(contrast) %>%
mutate(rank = 1:n()) %>%
filter(rank <= 100) -> topSets
dat %>% filter(set == "BROAD") %>%
filter(grepl("mmethyl", method)) %>%
arrange(contrast, method, pvalue) %>%
group_by(contrast, method, sub) %>%
mutate(csum = cumsum(ID %in% topSets$ID)) %>%
mutate(rank = 1:n()) %>%
mutate(cut = ifelse(sub == "c1", "Top 5000",
ifelse(sub == "c2", "Top 10000",
ifelse(sub == "p1", "FDR < 0.01", "FDR < 0.05")))) %>%
filter(rank <= 100) -> sub
p <- ggplot(sub, aes(x = rank, y = csum, colour = cut)) +
geom_line() +
facet_wrap(vars(contrast, method), ncol=2, nrow = 3, scales = "free") +
geom_vline(xintercept = 10, linetype = "dotted") +
labs(colour = "Sig. select", x = "Rank", y = "Cumulative no. immune sets") +
theme(legend.position = "bottom")
p
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /config/RStudio/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /config/RStudio/R/3.6.1/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ChAMPdata_2.18.0 patchwork_1.0.0
[3] rbin_0.2.0 forcats_0.4.0
[5] stringr_1.4.0 dplyr_0.8.5
[7] purrr_0.3.4 readr_1.3.1
[9] tidyr_1.0.3 tibble_2.1.3
[11] tidyverse_1.3.0 glue_1.4.1
[13] ggplot2_3.3.0 missMethyl_1.20.4
[15] gridExtra_2.3 reshape2_1.4.3
[17] limma_3.42.2 paletteer_1.1.0
[19] minfi_1.32.0 bumphunter_1.26.0
[21] locfit_1.5-9.1 iterators_1.0.12
[23] foreach_1.5.0 Biostrings_2.54.0
[25] XVector_0.24.0 SummarizedExperiment_1.16.1
[27] DelayedArray_0.12.3 BiocParallel_1.20.1
[29] matrixStats_0.56.0 Biobase_2.46.0
[31] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[33] IRanges_2.20.2 S4Vectors_0.24.4
[35] BiocGenerics_0.32.0 here_0.1
[37] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5
[2] RSQLite_2.1.2
[3] AnnotationDbi_1.46.1
[4] grid_3.6.1
[5] munsell_0.5.0
[6] codetools_0.2-16
[7] preprocessCore_1.48.0
[8] statmod_1.4.32
[9] withr_2.1.2
[10] colorspace_1.4-1
[11] knitr_1.28
[12] rstudioapi_0.11
[13] labeling_0.3
[14] git2r_0.27.1
[15] GenomeInfoDbData_1.2.1
[16] farver_2.0.3
[17] bit64_0.9-7
[18] rhdf5_2.30.1
[19] rprojroot_1.3-2
[20] vctrs_0.3.0
[21] generics_0.0.2
[22] xfun_0.13
[23] BiocFileCache_1.10.2
[24] R6_2.4.1
[25] illuminaio_0.28.0
[26] palr_0.2.0
[27] pals_1.6
[28] bitops_1.0-6
[29] reshape_0.8.8
[30] assertthat_0.2.1
[31] promises_1.1.0
[32] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
[33] scales_1.1.1
[34] gtable_0.3.0
[35] methylumi_2.30.0
[36] rlang_0.4.6
[37] genefilter_1.68.0
[38] splines_3.6.1
[39] rtracklayer_1.44.4
[40] GEOquery_2.54.1
[41] dichromat_2.0-0
[42] broom_0.5.2
[43] scico_1.1.0
[44] yaml_2.2.1
[45] modelr_0.1.7
[46] GenomicFeatures_1.36.4
[47] backports_1.1.7
[48] httpuv_1.5.2
[49] tools_3.6.1
[50] nor1mix_1.3-0
[51] RColorBrewer_1.1-2
[52] siggenes_1.60.0
[53] Rcpp_1.0.4.6
[54] plyr_1.8.6
[55] progress_1.2.2
[56] zlibbioc_1.30.0
[57] RCurl_1.95-4.12
[58] BiasedUrn_1.07
[59] prettyunits_1.0.2
[60] openssl_1.4.1
[61] IlluminaHumanMethylationEPICmanifest_0.3.0
[62] haven_2.2.0
[63] cluster_2.1.0
[64] fs_1.4.1
[65] magrittr_1.5
[66] data.table_1.12.8
[67] reprex_0.3.0
[68] whisker_0.4
[69] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
[70] hms_0.5.3
[71] evaluate_0.14
[72] xtable_1.8-4
[73] XML_3.98-1.20
[74] mclust_5.4.6
[75] readxl_1.3.1
[76] compiler_3.6.1
[77] biomaRt_2.42.1
[78] maps_3.3.0
[79] crayon_1.3.4
[80] htmltools_0.4.0
[81] later_1.0.0
[82] jcolors_0.0.4
[83] lubridate_1.7.4
[84] DBI_1.0.0
[85] dbplyr_1.4.2
[86] MASS_7.3-51.6
[87] rappdirs_0.3.1
[88] Matrix_1.2-18
[89] cli_2.0.2
[90] quadprog_1.5-8
[91] pkgconfig_2.0.3
[92] GenomicAlignments_1.20.1
[93] registry_0.5-1
[94] IlluminaHumanMethylation450kmanifest_0.4.0
[95] oompaBase_3.2.9
[96] xml2_1.3.2
[97] annotate_1.62.0
[98] rngtools_1.4
[99] pkgmaker_0.27
[100] multtest_2.40.0
[101] beanplot_1.2
[102] ruv_0.9.7.1
[103] rvest_0.3.5
[104] bibtex_0.4.2
[105] doRNG_1.7.1
[106] scrime_1.3.5
[107] digest_0.6.25
[108] rmarkdown_2.1
[109] base64_2.0
[110] cellranger_1.1.0
[111] DelayedMatrixStats_1.8.0
[112] curl_4.3
[113] Rsamtools_2.0.1
[114] lifecycle_0.2.0
[115] nlme_3.1-147
[116] jsonlite_1.6.1
[117] Rhdf5lib_1.6.1
[118] mapproj_1.2.6
[119] fansi_0.4.1
[120] viridisLite_0.3.0
[121] askpass_1.1
[122] pillar_1.4.4
[123] lattice_0.20-41
[124] httr_1.4.1
[125] survival_2.44-1.1
[126] GO.db_3.8.2
[127] bit_1.1-14
[128] stringi_1.4.6
[129] HDF5Array_1.14.4
[130] rematch2_2.1.0
[131] blob_1.2.0
[132] org.Hs.eg.db_3.8.2
[133] memoise_1.1.0