This section contains the complete ELMER code for the following analysis:
Below is the complete code that was explained in the other sections.
library(MultiAssayExperiment)
library(ELMER.data)
library(ELMER)
# get distal probes that are 2kb away from TSS on chromosome 1
distal.probes <- get.feature.probe(genome = "hg19",
met.platform = "450K",
rm.chr = paste0("chr",c(2:22,"X","Y")))
data(LUSC_RNA_refined,package = "ELMER.data") # GeneExp
data(LUSC_meth_refined,package = "ELMER.data") # Meth
mae <- createMAE(exp = GeneExp,
met = Meth,
save = TRUE,
linearize.exp = TRUE,
save.filename = "mae.rda",
filter.probes = distal.probes,
met.platform = "450K",
genome = "hg19",
TCGA = TRUE)
group.col <- "definition"
group1 <- "Primary solid Tumor"
group2 <- "Solid Tissue Normal"
dir.out <- "result"
diff.dir <- "hypo" # Search for hypomethylated probes in group 1
sig.diff <- get.diff.meth(data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
minSubgroupFrac = 0.2,
sig.dif = 0.3,
diff.dir = diff.dir,
cores = 1,
dir.out = dir.out,
pvalue = 0.01)
nearGenes <- GetNearGenes(data = mae,
probes = sig.diff$probe,
numFlankingGenes = 20) # 10 upstream and 10 dowstream genes
pair <- get.pair(data = mae,
group.col = group.col,
group1 = group1,
mode = "unsupervised",
group2 = group2,
nearGenes = nearGenes,
diff.dir = diff.dir,
minSubgroupFrac = 0.4, # % of samples to use in to create groups U/M
permu.dir = file.path(dir.out,"permu"),
permu.size = 100, # Please set to 100000 to get significant results
raw.pvalue = 0.05,
Pe = 0.01, # Please set to 0.001 to get significant results
filter.probes = TRUE, # See preAssociationProbeFiltering function
filter.percentage = 0.05,
filter.portion = 0.3,
dir.out = dir.out,
cores = 1,
label = diff.dir)
# Identify enriched motif for significantly hypomethylated probes which
# have putative target genes.
enriched.motif <- get.enriched.motif(data = mae,
probes = pair$Probe,
dir.out = dir.out,
label = diff.dir,
min.incidence = 10,
lower.OR = 1.1)
TF <- get.TFs(data = mae,
mode = "unsupervised",
group.col = group.col,
group1 = group1,
group2 = group2,
enriched.motif = enriched.motif,
dir.out = dir.out,
cores = 1,
label = diff.dir)library(stringr)
library(TCGAbiolinks)
library(dplyr)
library(ELMER)
library(MultiAssayExperiment)
library(parallel)
library(readr)
dir.create("~/paper_elmer/",showWarnings = FALSE)
setwd("~/paper_elmer/")
file <- "mae_BRCA_hg38_450K_no_ffpe.rda"
if(file.exists(file)) {
mae <- get(load(file))
} else {
getTCGA(disease = "BRCA", # TCGA disease abbreviation (BRCA,BLCA,GBM, LGG, etc)
basedir = "DATA", # Where data will be downloaded
genome = "hg38") # Genome of refenrece "hg38" or "hg19"
distal.probes <- get.feature.probe(feature = NULL,
genome = "hg38",
met.platform = "450K")
mae <- createMAE(exp = "~/paper_elmer/Data/BRCA/BRCA_RNA_hg38.rda",
met = "~/paper_elmer/Data/BRCA/BRCA_meth_hg38.rda",
met.platform = "450K",
genome = "hg38",
linearize.exp = TRUE,
filter.probes = distal.probes,
met.na.cut = 0.2,
save = FALSE,
TCGA = TRUE)
# Remove FFPE samples from the analysis
mae <- mae[,!mae$is_ffpe]
# Get molecular subytpe information from cell paper and more metadata (purity etc...)
# https://doi.org/10.1016/j.cell.2015.09.033
file <- "http://ars.els-cdn.com/content/image/1-s2.0-S0092867415011952-mmc2.xlsx"
downloader::download(file, basename(file))
subtypes <- readxl::read_excel(basename(file), skip = 2)
subtypes$sample <- substr(subtypes$Methylation,1,16)
meta.data <- merge(colData(mae),subtypes,by = "sample",all.x = T)
meta.data <- meta.data[match(colData(mae)$sample,meta.data$sample),]
meta.data <- S4Vectors::DataFrame(meta.data)
rownames(meta.data) <- meta.data$sample
stopifnot(all(meta.data$patient == colData(mae)$patient))
colData(mae) <- meta.data
save(mae, file = "mae_BRCA_hg38_450K_no_ffpe.rda")
}
dir.out <- "BRCA_unsupervised_hg38/hypo"
cores <- 10
diff.probes <- get.diff.meth(data = mae,
group.col = "definition",
group1 = "Primary solid Tumor",
group2 = "Solid Tissue Normal",
diff.dir = "hypo", # Get probes hypometh. in group 1
cores = cores,
minSubgroupFrac = 0.2, # % group samples used.
pvalue = 0.01,
sig.dif = 0.3,
dir.out = dir.out,
save = TRUE)
# For each differently methylated probes we will get the
# 20 nearby genes (10 downstream and 10 upstream)
nearGenes <- GetNearGenes(data = mae,
probes = diff.probes$probe,
numFlankingGenes = 20)
# This step is the most time consuming. Depending on the size of the groups
# and the number of probes found previously it migh take hours
Hypo.pair <- get.pair(data = mae,
nearGenes = nearGenes,
group.col = "definition",
group1 = "Primary solid Tumor",
group2 = "Solid Tissue Normal",
permu.dir = paste0(dir.out,"/permu"),
permu.size = 10000,
mode = "unsupervised",
minSubgroupFrac = 0.4, # 40% of samples to create U and M
raw.pvalue = 0.001,
Pe = 0.001,
filter.probes = TRUE,
filter.percentage = 0.05,
filter.portion = 0.3,
dir.out = dir.out,
cores = cores,
label = "hypo")
# Number of pairs: 2950
enriched.motif <- get.enriched.motif(data = mae,
min.motif.quality = "DS",
probes = unique(Hypo.pair$Probe),
dir.out = dir.out,
label = "hypo",
min.incidence = 10,
lower.OR = 1.1)
TF <- get.TFs(data = mae,
group.col = "definition",
group1 = "Primary solid Tumor",
group2 = "Solid Tissue Normal",
minSubgroupFrac = 0.4, # Set to 1 if supervised mode
enriched.motif = enriched.motif,
dir.out = dir.out,
cores = cores,
label = "hypo")library(stringr)
library(TCGAbiolinks)
library(dplyr)
library(ELMER)
library(MultiAssayExperiment)
library(parallel)
library(readr)
#-----------------------------------
# 1 - Samples
# ----------------------------------
dir.create("~/paper_elmer/",showWarnings = FALSE)
setwd("~/paper_elmer/")
file <- "mae_BRCA_hg38_450K_no_ffpe.rda"
if(file.exists(file)) {
mae <- get(load(file))
} else {
getTCGA(disease = "BRCA", # TCGA disease abbreviation (BRCA,BLCA,GBM, LGG, etc)
basedir = "DATA", # Where data will be downloaded
genome = "hg38") # Genome of refenrece "hg38" or "hg19"
distal.probes <- get.feature.probe(feature = NULL,
genome = "hg38",
met.platform = "450K")
mae <- createMAE(exp = "DATA/BRCA/BRCA_RNA_hg38.rda",
met = "DATA/BRCA/BRCA_meth_hg38.rda",
met.platform = "450K",
genome = "hg38",
linearize.exp = TRUE,
filter.probes = distal.probes,
met.na.cut = 0.2,
save = FALSE,
TCGA = TRUE)
# Remove FFPE samples from the analysis
mae <- mae[,!mae$is_ffpe]
# Get molecular subytpe information from cell paper and more metadata (purity etc...)
# https://doi.org/10.1016/j.cell.2015.09.033
file <- "http://ars.els-cdn.com/content/image/1-s2.0-S0092867415011952-mmc2.xlsx"
downloader::download(file, basename(file))
subtypes <- readxl::read_excel(basename(file), skip = 2)
subtypes$sample <- substr(subtypes$Methylation,1,16)
meta.data <- merge(colData(mae),subtypes,by = "sample",all.x = T)
meta.data <- meta.data[match(colData(mae)$sample,meta.data$sample),]
meta.data <- S4Vectors::DataFrame(meta.data)
rownames(meta.data) <- meta.data$sample
stopifnot(all(meta.data$patient == colData(mae)$patient))
colData(mae) <- meta.data
save(mae, file = "mae_BRCA_hg38_450K_no_ffpe.rda")
}
cores <- 6
direction <- c( "hypo","hyper")
genome <- "hg38"
group.col <- "PAM50"
groups <- t(combn(na.omit(unique(colData(mae)[,group.col])),2))
for(g in 1:nrow(groups)) {
group1 <- groups[g,1]
group2 <- groups[g,2]
for (j in direction){
tryCatch({
message("Analysing probes ",j, "methylated in ", group1, " vs ", group2)
dir.out <- paste0("BRCA_supervised_",genome,"/",group1,"_",group2,"/",j)
dir.create(dir.out, recursive = TRUE)
#--------------------------------------
# STEP 3: Analysis |
#--------------------------------------
# Step 3.1: Get diff methylated probes |
#--------------------------------------
Sig.probes <- get.diff.meth(data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
sig.dif = 0.3,
minSubgroupFrac = 1,
cores = cores,
dir.out = dir.out,
diff.dir = j,
pvalue = 0.01)
if(nrow(Sig.probes) == 0) next
#-------------------------------------------------------------
# Step 3.2: Identify significant probe-gene pairs |
#-------------------------------------------------------------
# Collect nearby 20 genes for Sig.probes
nearGenes <- GetNearGenes(data = mae,
probe = Sig.probes$probe)
pair <- get.pair(data = mae,
nearGenes = nearGenes,
group.col = group.col,
group1 = group1,
group2 = group2,
permu.dir = paste0(dir.out,"/permu"),
dir.out = dir.out,
mode = "supervised",
diff.dir = j,
cores = cores,
label = j,
permu.size = 10000,
raw.pvalue = 0.001)
Sig.probes.paired <- read.csv(paste0(dir.out,
"/getPair.",j,
".pairs.significant.csv"),
stringsAsFactors=FALSE)[,1]
#-------------------------------------------------------------
# Step 3.3: Motif enrichment analysis on the selected probes |
#-------------------------------------------------------------
if(length(Sig.probes.paired) > 0 ){
#-------------------------------------------------------------
# Step 3.3: Motif enrichment analysis on the selected probes |
#-------------------------------------------------------------
enriched.motif <- get.enriched.motif(probes = Sig.probes.paired,
dir.out = dir.out,
data = mae,
label = j,
plot.title = paste0("BRCA: OR for paired probes ",
j, "methylated in ",
group1, " vs ",group2))
motif.enrichment <- read.csv(paste0(dir.out,
"/getMotif.",j,
".motif.enrichment.csv"),
stringsAsFactors=FALSE)
if(length(enriched.motif) > 0){
#-------------------------------------------------------------
# Step 3.4: Identifying regulatory TFs |
#-------------------------------------------------------------
print("get.TFs")
TF <- get.TFs(data = mae,
enriched.motif = enriched.motif,
dir.out = dir.out,
mode = "supervised",
group.col = group.col,
group1 = group1,
diff.dir = j,
group2 = group2,
cores = cores,
label = j)
TF.meth.cor <- get(load(paste0(dir.out,
"/getTF.",j,
".TFs.with.motif.pvalue.rda")))
save(mae,TF, enriched.motif, Sig.probes.paired,
pair, nearGenes, Sig.probes, motif.enrichment,
TF.meth.cor,
file=paste0(dir.out,"/ELMER_results_",j,".rda"))
}
}
}, error = function(e){
message(e)
})
}
}