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ase_nomatch_aml.R
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## analysis pipeline
library(readr)
library(VariantAnnotation)
library(BSgenome.Hsapiens.1000genomes.hs37d5)
bsgenome_hs37d5 <- BSgenome.Hsapiens.1000genomes.hs37d5
library(BSgenome.Hsapiens.UCSC.hg19)
bsgenome_hg19 <- BSgenome.Hsapiens.UCSC.hg19
library(biomaRt)
library(ggplot2)
library(parallel)
library(VGAM)
library(rslurm)
source(file = "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/ASE_analysis/1000Genomes_getAllelecounts.R")
source(file = "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/ASE_analysis/utils.R")
## statics
ALLELECOUNTER <- "/srv/sw/eb/software/alleleCount/4.0.0-GCCcore-6.4.0/bin/alleleCounter"
ALLELESDIR <- "/srv/shared/vanloo/pipeline-files/human/references/1000genomes/1000genomes_20130501_v5b/"
JAVA <- "/srv/sw/eb/software/Java/1.8.0_162/bin/java"
# sampledf <- read.delim(file = "20171108_complete_samples", as.is = T)
# sampledf <- read.delim(file = "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/results/20180626_aml_samples.txt", as.is = T)
# sampledf$id <- paste0(sampledf$No., sampledf$Initials)
# sampledf <- rbind(sampledf, c(Number = "CD34", 'No.' = "", Initials = "", id = "CD34"))
# sampledf <- sampledf[!is.na(sampledf$n_wes_id), ]
# sampledf[4,]
### functions
####### read in loci from the matched normal exome, keep only the clean heterozygous ones
#
# ## collect allelecounts for ref and alt, keep only those sites exceeding minimal depth requirement
# get_alleles_chr <- function(chr, allelesdir, countsdir, outdir, mindepth = 10, sample_id) {
# # allelesfile <- "/srv/shared/vanloo/pipeline-files/human/references/1000genomes/1000genomes_20130501_v5b/1000genomesAlleles2013v5b_chr22.txt"
# # countsfile <- "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/ASE_analysis/WES_1/WES_1_alleleCounts_chr22.txt"
# # mindepth <- 10
# allelesfile <- file.path(allelesdir, paste0("1000genomesAlleles2013v5b_chr", chr, ".txt"))
# countsfile <- file.path(countsdir, paste0(sample_id, "_alleleCounts_chr", chr, ".txt"))
# outfile <- file.path(outdir, paste0(sample_id, "_inform_alleles_chr", chr, ".txt"))
#
# alleles <- read_tsv(file = allelesfile, col_names = T, col_types = "cicc")
# counts <- read_tsv(file = countsfile, col_names = c("chr", "pos", "count_A", "count_C", "count_G", "count_T", "depth"), col_types = "ciiiiii", comment = "#")
# alleles$count_ref <- ifelse(alleles$ref == "A", counts$count_A,
# ifelse(alleles$ref == "C", counts$count_C,
# ifelse(alleles$ref == "G", counts$count_G, counts$count_T)))
# alleles$count_alt <- ifelse(alleles$alt == "A", counts$count_A,
# ifelse(alleles$alt == "C", counts$count_C,
# ifelse(alleles$alt == "G", counts$count_G, counts$count_T)))
#
# output <- alleles[rowSums(alleles[, c("count_ref", "count_alt")]) >= mindepth, ]
# write_tsv(x = output, path = outfile, col_names = F)
# return(NULL)
# }
#
#
# ## further filter allelecounts to retain only confident heterozygous ones, write output into hetSNPs file
# ## also write loci to feed to AlleleCount for tumour exome, write vcf to feed to ASEReadCounter for tumour RNA
# construct_new_loci <- function(countsdir, outdir, alpha = .1, lower = 1/3, mid = .5, upper = 2/3, sample_id, genome = bsgenome_hg19) {
#
# allelecounts_files <- list.files(path = countsdir, pattern = "_inform_alleles_chr", full.names = T)
# allelecounts <- do.call(rbind, lapply(X = allelecounts_files, FUN = read_tsv, col_names = c("chr", "pos", "ref", "alt", "count_ref", "count_alt"), col_types = "ciccii"))
#
# allelecounts <- allelecounts[allelecounts$count_ref > 0 & allelecounts$count_alt > 0, ]
# pconfint <- apply(X = allelecounts[, c("count_ref", "count_alt")], MARGIN = 1, FUN = function(x) qbeta(c(alpha/2, 1-alpha/2), shape1 = x[1]+1, shape2 = x[2]+1, lower.tail = TRUE, log.p = FALSE))
# is_het <- pconfint[1,] >= lower & pconfint[1,] < mid & pconfint[2,] <= upper & pconfint[2,] > mid
#
# outfile <- file.path(outdir, paste0(sample_id, "_hetSNPs.txt"))
# locifile <- file.path(outdir, paste0(sample_id, "_hetSNPs_loci.txt"))
# allelecounts <- allelecounts[is_het, ]
# write_tsv(x = allelecounts, path = outfile, col_names = T)
# write_tsv(x = allelecounts[ , c("chr", "pos")], path = locifile, col_names = F)
#
# locivcf <- file.path(outdir, paste0(sample_id, "_hetSNPs.vcf"))
# ## annoyingly, transcriptome is mapped to hg19, while exomes are mapped to 1000G_GRCh37d5 (positions of main chroms match, but names differ of course)
# allelecounts_vr <- VRanges(seqnames = paste0("chr", allelecounts$chr), ranges = IRanges(start = allelecounts$pos, end = allelecounts$pos), ref = allelecounts$ref, alt = allelecounts$alt, seqinfo = seqinfo(genome))
# allelecounts_vr <- sort(allelecounts_vr)
# sampleNames(allelecounts_vr) <- sample_id
# writeVcf(obj = allelecounts_vr, filename = locivcf)
#
# return(NULL)
# }
## collect allelecounts for ref and alt, keep only those sites exceeding minimal depth requirement for each
get_alleles_chr_nomatch <- function(chr, allelesdir, countsdir, mindepth = 3, sample_id) {
allelesfile <- file.path(allelesdir, paste0("1000genomesAlleles2013v5b_chr", chr, ".txt"))
countsfile <- file.path(countsdir, paste0(sample_id, "_alleleCounts_chr", chr, ".txt"))
outfile_allelecounts <- file.path(countsdir, paste0(sample_id, "_inform_alleles_nomatch_chr", chr, ".txt"))
alleles <- read_tsv(file = allelesfile, col_names = T, col_types = "cicc")
counts <- read_tsv(file = countsfile, col_names = c("chr", "pos", "count_A", "count_C", "count_G", "count_T", "depth"), col_types = "ciiiiii", comment = "#")
alleles$count_ref <- ifelse(alleles$ref == "A", counts$count_A,
ifelse(alleles$ref == "C", counts$count_C,
ifelse(alleles$ref == "G", counts$count_G, counts$count_T)))
alleles$count_alt <- ifelse(alleles$alt == "A", counts$count_A,
ifelse(alleles$alt == "C", counts$count_C,
ifelse(alleles$alt == "G", counts$count_G, counts$count_T)))
output <- alleles[alleles$count_ref >= mindepth & alleles$count_alt >= mindepth, ]
write_tsv(x = output, path = outfile_allelecounts, col_names = F)
return(NULL)
}
combine_loci_nomatch <- function(countsdir, sample_id) {
allelecounts_files <- list.files(path = countsdir, pattern = "_inform_alleles_nomatch_chr", full.names = T)
allelecounts <- do.call(rbind, lapply(X = allelecounts_files, FUN = read_tsv, col_names = c("chr", "pos", "ref", "alt", "count_ref", "count_alt"), col_types = "ciccii"))
outfile <- file.path(countsdir, paste0(sample_id, "_hetSNPs_nomatch.txt"))
write_tsv(x = allelecounts, path = outfile, col_names = T)
locivcf <- file.path(countsdir, paste0(sample_id, "_hetSNPs_nomatch.vcf"))
## annoyingly, transcriptome is mapped to hg19, while exomes are mapped to 1000G_GRCh37d5 (positions of main chroms match, but names differ of course)
if (any(grepl(pattern = "^chr", x = allelecounts$chr[sample(x = 1:nrow(allelecounts), size = 100, replace = T)]))) {
allelecounts_vr <- VRanges(seqnames = allelecounts$chr, ranges = IRanges(start = allelecounts$pos, end = allelecounts$pos), ref = allelecounts$ref, alt = allelecounts$alt, seqinfo = seqinfo(bsgenome_hs37d5))
} else {
allelecounts_vr <- VRanges(seqnames = paste0("chr", allelecounts$chr), ranges = IRanges(start = allelecounts$pos, end = allelecounts$pos), ref = allelecounts$ref, alt = allelecounts$alt, seqinfo = seqinfo(bsgenome_hg19))
}
allelecounts_vr <- sort(allelecounts_vr)
sampleNames(allelecounts_vr) <- sample_id
writeVcf(obj = allelecounts_vr, filename = locivcf, index = T)
return(NULL)
}
## Use ASEReadCounter to get allelecounts on the RNA
ASEReadCount <- function(hetSNPvcf, bamfile, refgenome, outfile, minBaseQ = 20, minMapQ = 35) {
cmd <- paste0(JAVA, " -jar /srv/sw/eb/software/GATK/3.8-1-Java-1.8.0_162/GenomeAnalysisTK.jar",
" -R ", refgenome,
" -T ASEReadCounter",
" -o ", outfile,
" -I ", bamfile,
" -sites ", hetSNPvcf,
" -U ALLOW_N_CIGAR_READS",
" -minDepth 1",
" --minMappingQuality ", minMapQ,
" --minBaseQuality ", minBaseQ,
" --countOverlapReadsType COUNT_FRAGMENTS_REQUIRE_SAME_BASE")
system(cmd, wait = T)
# return(cmd)
}
## read in loci from the tumour exome, get counts and construct Beta distrib (theta)
## then read in loci from the tumour transcriptome, compute Betabin(X>x|theta)
compute_pvals_nomatch <- function(toutdir, tsample, filtercutoff = 0.01, exclude_bad_snps = F) {
asecountsfile <- file.path(toutdir, paste0(tsample, "_asereadcounts_nomatch.rtable"))
genomecountsfile <- file.path(toutdir, paste0(tsample, "_hetSNPs_nomatch.txt"))
asecounts <- read_tsv(file = asecountsfile, col_names = T, col_types = "ciccciiiiiiii")
genomecounts <- read_tsv(file = genomecountsfile, col_types = "ciccii")
colnames(genomecounts) <- c("chr", "pos", "ref", "alt", "refCountGenome", "altCountGenome")
asecounts$contig <- sub(pattern = "chr", replacement = "", x = asecounts$contig)
asedf <- merge(x = asecounts, y = genomecounts, by.x = c("contig", "position"), by.y = c("chr", "pos"))
asedf <- asedf[ , c("contig", "position", "refAllele", "altAllele", "refCountGenome", "altCountGenome",
"refCount", "altCount")]
asedf$filter <- apply(X = asedf[, c("refCountGenome", "altCountGenome", "refCount", "altCount")], MARGIN = 1,
FUN = function(x) min(betabinom.test.ab(q = 0, size = x["refCount"] + x["altCount"], shape1 = x["refCountGenome"] + 1, shape2 = x["altCountGenome"] + 1, alternative = "two.sided"),
betabinom.test.ab(q = x["refCount"] + x["altCount"], size = x["refCount"] + x["altCount"], shape1 = x["refCountGenome"] + 1, shape2 = x["altCountGenome"] + 1, alternative = "two.sided")))
asedf$pval <- apply(X = asedf[, c("refCountGenome", "altCountGenome", "refCount", "altCount")], MARGIN = 1,
FUN = function(x) betabinom.test.ab(q = x["refCount"], size = x["refCount"] + x["altCount"], shape1 = x["refCountGenome"] + 1, shape2 = x["altCountGenome"] + 1, alternative = "two.sided"))
asedf$padj <- 1
is_testworthy <- asedf$filter <= filtercutoff
if (exclude_bad_snps) {
probloci <- read_tsv(file = "/srv/shared/vanloo/pipeline-files/human/references/battenberg/battenberg_problem_loci/probloci_270415.txt.gz", col_types = "ci")
isbadsnp <- paste0(asedf$contig, "_", asedf$position) %in% paste0(probloci$Chr, "_", probloci$Pos)
is_testworthy <- is_testworthy & !isbadsnp
}
# asedf$is_testworthy <- asedf$filter <= filtercutoff
asedf[is_testworthy, "padj"] <- p.adjust(asedf[is_testworthy, "pval"], method = "fdr")
return(asedf)
}
ase_annotate <- function(asedf) {
chromregions <- paste(asedf$contig, asedf$position, asedf$position, sep = ":", collapse = ",")
# identify and get data from ensembl74 biomart
# listMarts(host="grch37.ensembl.org")
# ensembl=useMart(host="www.ensembl.org",biomart = "ENSEMBL_MART_ENSEMBL")
# listDatasets(ensembl)
ensembl <- useMart(host = "grch37.ensembl.org", biomart = "ENSEMBL_MART_ENSEMBL", dataset = "hsapiens_gene_ensembl")
# filters <- listFilters(ensembl)
# attributes <- listAttributes(ensembl)
annot <- getBM(attributes = c("chromosome_name", "start_position", "end_position", "external_gene_name"),
filters = c("chromosomal_region"), mart = ensembl, values = chromregions)
asegr <- GRanges(seqnames = asedf$contig, ranges = IRanges(start = asedf$position, end = asedf$position),
mcols = asedf[, -c(1,2)], seqinfo = seqinfo(bsgenome_hs37d5))
annotgr <- GRanges(seqnames = annot$chromosome_name, ranges = IRanges(start = annot$start_position, end = annot$end_position),
mcols = annot[, -c(1:3)], seqinfo = seqinfo(bsgenome_hs37d5))
annothits <- findOverlaps(query = asegr, subject = annotgr)
asedf$gene <- NA
asedf[unique(queryHits(annothits)), "gene"] <- do.call(rbind, by(data = annot[subjectHits(annothits), "external_gene_name"], INDICES = queryHits(annothits),
FUN = function(x) data.frame(genes = paste0(unique(x), collapse = ","), stringsAsFactors = F)))
return(asedf)
}
plot_ase_manhattan <- function(asedf) {
labeldf <- data.frame(chr = c(1:22,"X"), pos = as.vector(by(data = 1:nrow(asedf), INDICES = asedf$contig, FUN = mean)), brks = as.vector(by(data = 1:nrow(asedf), INDICES = asedf$contig, FUN = max)))
p1 <- ggplot(data = asedf, mapping = aes(x = 1:nrow(asedf), y = -log10(pval))) + geom_point(mapping = aes(colour = contig %in% as.character(seq(2,22,2))), show.legend = F, alpha = .3, size = .5) +
geom_hline(yintercept = -log10(max(asedf[asedf$padj < .05, "pval"])), linetype = "dashed", colour = "grey") +
geom_text(data = asedf[asedf$padj <= .05, ], mapping = aes(x = which(asedf$padj <= .05), y = -log10(pval), label = gene), size = 1.5, angle = 45, hjust = 0, nudge_x = nrow(asedf)/250, nudge_y = 0.1, alpha = .5, show.legend = F)
p1 <- p1 + scale_x_continuous(breaks = labeldf$pos, labels = labeldf$chr, minor_breaks = labeldf$brks) + scale_y_continuous(breaks = seq(0,10,2), oob = scales::squish, limits = c(0,10))
p1 <- p1 + theme_minimal() + theme(panel.grid.major.x = element_blank()) + labs(x = NULL)
return(p1)
}
### functions
get_ase_aml_cell <- function(SAMPLEID) {
# for (SAMPLEID in sampledf$id) {
# for (SAMPLEID in sampledf[1:nrow(sampledf), "sampleid"]) {
# for (i in 2:nrow(sampledf)) {
# i <- 7
## required input
# SAMPLEID <- sampledf[i, "id"]
# TWESID <- paste0("WES_", sampledf[i, "t_wes_id"])
TWESID <- SAMPLEID
# TWESID <- sampledf[i, "Number"]
# MWESID <- paste0("WES_", sampledf[i, "n_wes_id"])
print(TWESID)
# EXOMEDIR <- "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/data/Exome_Data/"
# EXOMEDIR <- "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/data/AML/exomes/patients/mapped"
# RNADIR <- "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/data/AML/RNA/patients/mapped"
EXOMEDIR <- "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/data/AML/exomes/cell_lines/mapped/"
RNADIR <- "/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/data/AML/RNA/cell_lines/mapped/"
# MBAMFILE <- list.files(path = EXOMEDIR, pattern = paste0(MWESID, "_.*bam$"), recursive = T, full.names = T)
TBAMFILE <- list.files(path = EXOMEDIR, pattern = paste0(SAMPLEID, ".*bam$"), recursive = T, full.names = T)
TRNABAMFILE <- list.files(path = RNADIR, pattern = paste0(TWESID, ".*bam$"), recursive = T, full.names = T)
RNAREFGENOME <- "/srv/shared/vanloo/pipeline-files/human/references/alignment/hg19/ucsc.hg19.fasta"
# MOUTDIR <- paste0("/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/ASE_analysis/", MWESID)
TOUTDIR <- paste0("/srv/shared/vanloo/home/jdemeul/projects/2016_mansour_ASE_T-ALL/results/AML_ase/", TWESID)
minMapQ <- 35
minBaseQ <- 20
NCORES <- 1
## get allelecounts for matched normal exome
chrs <- c(1:22, "X")
# dir.create(path = MOUTDIR)
dir.create(path = TOUTDIR)
# mcmapply(FUN = alleleCount,
# locifile = paste0(ALLELESDIR, "1000genomesloci2013v5b_chr", chrs, ".txt"),
# outfile = file.path(MOUTDIR, paste0(MWESID, "_alleleCounts_chr", chrs, ".txt")),
# MoreArgs = list(bam = MBAMFILE, min_baq = minBaseQ, min_maq = minMapQ),
# mc.cores = NCORES)
## get allelecounts for tumour exome
if (!file.exists(file.path(TOUTDIR, paste0(SAMPLEID, "_hetSNPs_nomatch.vcf.bgz")))) {
mcmapply(FUN = alleleCount,
locifile = paste0(ALLELESDIR, "1000genomesloci2013v5b_chr", chrs, ".txt"),
outfile = file.path(TOUTDIR, paste0(TWESID, "_alleleCounts_chr", chrs, ".txt")),
MoreArgs = list(bam = TBAMFILE, min_baq = minBaseQ, min_maq = minMapQ),
mc.cores = NCORES)
# mclapply(X = chrs, FUN = get_alleles_chr, allelesdir = ALLELESDIR, countsdir = MOUTDIR, outdir = MOUTDIR, sample_id = MWESID, mc.cores = NCORES)
# debug(construct_new_loci)
# construct_new_loci(countsdir = MOUTDIR, outdir = MOUTDIR, sample_id = MWESID)
mclapply(X = chrs, FUN = get_alleles_chr_nomatch, allelesdir = ALLELESDIR, countsdir = TOUTDIR, sample_id = TWESID, mc.cores = NCORES)
combine_loci_nomatch(countsdir = TOUTDIR, sample_id = TWESID)
}
## run allelecount on the new hetloci for tumour
# alleleCount(locifile = file.path(MOUTDIR, paste0(MWESID, "_hetSNPs_loci.txt")),
# bam = TBAMFILE,
# outfile = file.path(TOUTDIR, paste0(TWESID, "_hetSNPs_allelecounts.txt")),
# min_baq = minBaseQ, min_maq = minMapQ)
# RNAREFGENOME <- "/srv/shared/vanloo/pipeline-files/human/references/alignment/hg19/ucsc.hg19.fasta"
# outfile <- file.path(TOUTDIR, paste0(TWESID, "_asereadcounts.rtable"))
# bamfile <- TRNABAMFILE
# hetSNPvcf <- file.path(MOUTDIR, paste0(MWESID, "_hetSNPs.vcf"))
# minMapQ <- 35
# minBaseQ <- 20
if (!file.exists(file.path(TOUTDIR, paste0(SAMPLEID, "_asereadcounts_nomatch.rtable")))) {
ASEReadCount(hetSNPvcf = file.path(TOUTDIR, paste0(TWESID, "_hetSNPs_nomatch.vcf.bgz")),
bamfile = TRNABAMFILE,
refgenome = RNAREFGENOME,
outfile = file.path(TOUTDIR, paste0(TWESID, "_asereadcounts_nomatch.rtable")),
minBaseQ = minBaseQ, minMapQ = minMapQ)
}
# mcmapply(hetSNPvcf = file.path(TOUTDIR, paste0(TWESID, "_hetSNPs_nomatch_chr", chrs, ".vcf")),
# outfile = file.path(TOUTDIR, paste0(TWESID, "_asereadcounts_chr", chrs, ".rtable")),
# FUN = ASEReadCount, MoreArgs = list(bamfile = TRNABAMFILE, refgenome = RNAREFGENOME, minBaseQ = minBaseQ, minMapQ = minMapQ),
# mc.cores = NCORES, )
asedf <- compute_pvals_nomatch(toutdir = TOUTDIR, tsample = TWESID, exclude_bad_snps = F)
# write_tsv(x = asedf, path = file.path(TOUTDIR, paste0(TWESID, "_ase_out_all_noannot.txt")))
## Some QC
# assess filtering
options(bitmapType = "cairo")
p2 <- ggplot(data = asedf, mapping = aes(x = pval, fill = filter <= 0.01)) + geom_histogram(binwidth = 0.01) + scale_y_log10()
# p2
ggsave(filename = file.path(TOUTDIR, paste0(TWESID, "_filter.png")), plot = p2, dpi = 300, width = 10, height = 7)
# sum(asedf$padj < .05)
# ggd.qqplot(asedf[asedf$pval > 0, "pval"])
# ggd.qqplot(p.adjust(asedf[asedf$pval > 0, "pval"], method = "fdr"))
p3 <- ggqq(asedf[asedf$pval > 0, "pval"])
# p3
ggsave(filename = file.path(TOUTDIR, paste0(TWESID, "_QQ.png")), plot = p3, dpi = 300, width = 10, height = 7)
## Gene annotations:
asedf <- merge(x = asedf, y = ase_annotate(asedf = asedf[asedf$pval <= 0.01, ]), all.x = T)
asedf$contig <- factor(x = asedf$contig, levels = c(1:22, "X"))
asedf <- asedf[order(asedf$contig, asedf$position), ]
write_tsv(x = asedf, path = file.path(TOUTDIR, paste0(TWESID, "_ase_out.txt")))
## Plotting
p4 <- plot_ase_manhattan(asedf = asedf)
# p4
ggsave(filename = file.path(TOUTDIR, paste0(TWESID, "_manhattan.png")), plot = p4, dpi = 300, width = 10, height = 3)
return(NULL)
}
# sampledf <- data.frame(SAMPLEID = c("HL60", "MOLM16", "OCI_AML2", "OCI_AML3", "TF1", "THP1_S6", "THP1_S13" ))
# sampledf <- data.frame(SAMPLEID = c("OCI_AML3", "TF1"))
# debug(get_ase_aml_cell)
# get_ase_aml_cell(SAMPLEID = "THP1_S13")
amlasecelljob <- slurm_apply(f = get_ase_aml_cell, params = sampledf[,"SAMPLEID", drop = F], jobname = "ase_aml_cell", nodes = 2, cpus_per_node = 1, add_objects = ls(),
pkgs = rev(.packages()), libPaths = .libPaths(), slurm_options = list(), submit = T)
print_job_status(amlasecelljob)
# cancel_slurm(amlasecelljob)