R/FEVV.R
In isarnassiri/FEVV: FEVV: Functional Enrichment of Genomic Variants and Variations
Defines functions
querySNPsEnrichmentAnalysis
eSNPsEnrichmentAnalysis
Documented in
eSNPsEnrichmentAnalysis
querySNPsEnrichmentAnalysis
#############################################################################################
###################################### eSNP enrichment ######################################
#############################################################################################
#'@export
#'@import GenomicRanges
#'@import data.table
#'@export
#'@name eSNPsEnrichmentAnalysis
#'@title Functional Enrichment of eQTL SNPs
#'@description eQTL SNPs as input: for eSNP enrichment analysis, first, for each gene/transcript, we selected associated eSNPs as foreground (F) and SNPs within the 1Mb window around the TSS as background (B) SNP sets. We used the number of overlaps of foreground (f) and background (b) SNP sets in the genomic feature or chromatin state and calculate the enrichment score (z-score and odds ratio).
#'@author {Isar Nassiri, Benjamin Fairfax}
#'@param eQTL
#'eQTL profile including the following headers: seqnames, SNP_POS, SNP_POS, SNP_ID, gene_id
#'@param TranscriptName
#'Gene or isoform Ensembl ID
#'@param windowSize
#'window around the TSS (e.g. 1000000)
#'@param FDRthreshold
#'FDR threshold (e.g. 0.001)
#'@param BackendData_GenomicFeatures
#'fifteen-core chromatin states from https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/download/
#'@param BackendData_ChromatinStates
#'10 genomic features from the biomaRt (Ensembl) and UCSC
#'@param SNPs
#'List of all SNPs in human genome
#'@return You can find the results in R object under title of 'RESULTsGenomicFeatures' and 'RESULTsChromatinState'.
#'@examples
#'data(eQTL)
#'data(BackendData_GenomicFeatures)
#'data(BackendData_ChromatinStates)
#'data(SNPs)
#'eSNPsEnrichmentAnalysis(eQTL, TranscriptName = 'ENSG00000168310', windowSize=1000000, FDRthreshold = 0.001, BackendData_GenomicFeatures, BackendData_ChromatinStates, SNPs)
#'@export
eSNPsEnrichmentAnalysis <- NULL
eSNPsEnrichmentAnalysis <- function(eQTL, TranscriptName, windowSize, FDRthreshold, BackendData_GenomicFeatures, BackendData_ChromatinStates, SNPs)
{
zScore <- function (cntA, totA, cntB, totB) {
#calculate
avgProportion <- (cntA + cntB) / (totA + totB)
probA <- cntA/totA
probB <- cntB/totB
SE <- sqrt(avgProportion * (1-avgProportion)*(1/totA + 1/totB))
zScore <- (probA-probB) / SE
return (zScore)
}
#--------------------------- inputs
states_bed <- BackendData_ChromatinStates
chromatin_states <- GRanges(sample = states_bed[[4]],
seqnames = gsub('chr','',states_bed[[1]]),
ranges = IRanges(states_bed[[2]], states_bed[[3]]),
state = states_bed[[4]])
genomic_regions <- BackendData_GenomicFeatures
genomic_regions$seqnames <- gsub('chr','',genomic_regions$seqnames)
genomic_regions <- genomic_regions[which(genomic_regions$seqnames %in% c(1:22, 'X')),]
genomic_regions <- GRanges(sample = genomic_regions$sample,
seqnames = genomic_regions$seqnames,
ranges = IRanges(genomic_regions$start, genomic_regions$end),
state = genomic_regions$state)
SNPs_Imputed <- SNPs
SNPs_Imputed_subject <- GRanges(Rle(as.character(SNPs_Imputed$CHROM)), IRanges(SNPs_Imputed$POS, width=2), rsID = SNPs_Imputed$ID)
SNPs_Imputed <- as.data.frame(SNPs_Imputed)
eQTL <- eQTL[eQTL$FDR < FDRthreshold,]
eQTL <- eQTL[!is.na(eQTL$seqnames),]
eQTL <- as.data.frame(eQTL)
#--- select eQTLs
eQTL_sub <- eQTL[which(eQTL$gene_id == TranscriptName),]
dim(eQTL_sub)
#--- Foreground_rsIDs
Foreground_rsIDs <- GRanges(paste0(eQTL_sub$seqnames,':',eQTL_sub$SNP_POS,'-',eQTL_sub$SNP_POS), rsID = eQTL_sub$SNP_ID)
length(Foreground_rsIDs)
#--- Background_rsIDs - select SNPs any a 1Mb windows
posQuerySNP <- GRanges(paste0(eQTL_sub$seqnames[1],':',eQTL_sub$start[1],'-',eQTL_sub$start[1])) + windowSize
fo <- findOverlaps(query=posQuerySNP, subject=SNPs_Imputed_subject, type="any")
Background_rsIDs <- SNPs_Imputed_subject[subjectHits(fo),]
#--- remove foreground from background
Background_rsIDs <- Background_rsIDs[-which(Background_rsIDs$rsID %in% Foreground_rsIDs$rsID),]
length(Background_rsIDs)
#------------------- chromatin state
#--- enrich foreground
fo <- findOverlaps(query=Foreground_rsIDs, subject=chromatin_states, type="any")
foreground_CS <- chromatin_states[subjectHits(fo),]
#--- enrich background
fo <- findOverlaps(query=Background_rsIDs, subject=chromatin_states, type="any")
Background_CS <- chromatin_states[subjectHits(fo),]
CSs <- unique(foreground_CS$sample)
for(t in 1:length(CSs))
{
cntA<-length(which(foreground_CS$sample==CSs[t]))
totA<-length(Foreground_rsIDs)
cntB<-length(which(Background_CS$sample==CSs[t]))
totB<-length(Background_rsIDs)
table <- c(cntA, totA, cntB, totB)
dim(table)<-c(2,2)
fishert <- fisher.test(table)
z_score <- zScore(cntA, totA, cntB, totB)
temp <- data.frame(gene = TranscriptName, CS = CSs[t], p = fishert$p.value, oddsratio = fishert$estimate, CIl= fishert$conf.int[1], CIh = fishert$conf.int[2], zScore = z_score, FE = cntA, FA = totA, BE = cntB, BA = totB)
if(t==1){RESULTsChromatinState = temp}else{RESULTsChromatinState = rbind(RESULTsChromatinState, temp)}
}
#------------------- genomic features
#--- enrich foreground
fo <- findOverlaps(query=Foreground_rsIDs, subject=genomic_regions, type="any")
foreground_GR = genomic_regions[subjectHits(fo),]
#--- enrich background
fo <- findOverlaps(query=Background_rsIDs, subject=genomic_regions, type="any")
Background_GR = genomic_regions[subjectHits(fo),]
if(length(foreground_GR)>0)
{
GRs <- unique(foreground_GR$sample)
for(t in 1:length(GRs))
{
cntA<-length(which(foreground_GR$sample==GRs[t]))
totA<-length(foreground_GR)
cntB<-length(which(Background_GR$sample==GRs[t]))
totB<-length(Background_GR)
table <- c(cntA, totA, cntB, totB)
dim(table)<-c(2,2)
fishert <- fisher.test(table)
z_score <- zScore(cntA, totA, cntB, totB)
temp <- data.frame(gene = TranscriptName, GR = GRs[t], p = fishert$p.value, oddsratio = fishert$estimate, CIl= fishert$conf.int[1], CIh = fishert$conf.int[2], zScore = z_score, FE = cntA, FA = totA, BE = cntB, BA = totB)
if(t==1){RESULTsGenomicFeatures = temp}else{RESULTsGenomicFeatures = rbind(RESULTsGenomicFeatures, temp)}
}
}
#------------------- save outputs
Destiny_Folder <- system.file(package = "FEVV")
Destiny_Folder <- paste(Destiny_Folder, "/RESULTsGenomicFeatures.txt", sep = "")
write.table(
RESULTsGenomicFeatures, Destiny_Folder, sep = "\t", row.names = FALSE, quote = FALSE
)
Destiny_Folder <- system.file(package = "FEVV")
Destiny_Folder <- paste(Destiny_Folder, "/RESULTsChromatinState.txt", sep = "")
write.table(
RESULTsChromatinState, Destiny_Folder, sep = "\t", row.names = FALSE, quote = FALSE
)
print("You can find the results at: ")
print(system.file(package="FEVV"))
}
#########################################################################################################
###################################### single query SNP enrichment ######################################
#########################################################################################################
#'@export
#'@import SNPlocs.Hsapiens.dbSNP144.GRCh38
#'@import BSgenome.Hsapiens.UCSC.hg38
#'@import motifbreakR
#'@import snpStats
#'@import GenomicRanges
#'@import data.table
#'@import biomaRt
#'@import GenomicRanges
#'@import VariantAnnotation
#'@name querySNPsEnrichmentAnalysis
#'@title Single query SNP enrichment
#'@description a query SNP as the input: a query SNP as input: first, the 1MB window is defined for the query SNP, and the relevant variants captured from the 1000 genomes data in the European population background. Next, we split the SNPs based upon a LD (R?? = 0.8 and MAF = 0.01) to foreground (query SNP and its LD proxies) and background SNPs sets. We used the number of overlaps of foreground (f) and background (b) SNP sets in the genomic feature or chromatin state and calculate the enrichment score (z-score and odds ratio).
#'@author {Isar Nassiri, Benjamin Fairfax}
#'@param windowSize
#'window around the TSS (e.g. 1000000)
#'@param BackendData_GenomicFeatures
#'fifteen-core chromatin states from https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/download/
#'@param BackendData_ChromatinStates
#'10 genomic features from the biomaRt (Ensembl) and UCSC
#'@param SNP
#'a query SNP
#'@param mafThreshold
#'minor allele frequency (MAF) threshold
#'@param vcfMetaData
#'meta data of 1000 Genomes project (phase3) including 'sample', 'pop', 'super_pop', and 'gender' header
#'@param vcfPATH
#'Pathe to the vcf file.
#'@return You can find the results in R object under title of 'RESULTsGenomicFeatures' and 'RESULTsChromatinState'.
#'@examples
#'data(BackendData_GenomicFeatures)
#'data(BackendData_ChromatinStates)
#'Destiny_Folder <- system.file(package = "FEVV")
#'querySNPsEnrichmentAnalysis(SNP = 'rs13149699', mafThreshold = 0.039, windowSize = 1000000, BackendData_GenomicFeatures, BackendData_ChromatinStates, vcfMetaData = system.file("extdata", "Genotyping1000_samples_metatadata.txt", package="FEVV"), vcfPATH = 'http://hgdownload.cse.ucsc.edu/gbdb/hg19/1000Genomes/phase3/ALL.chr4.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz' )
#'@export
querySNPsEnrichmentAnalysis <- NULL
querySNPsEnrichmentAnalysis <- function(SNP, mafThreshold, windowSize, BackendData_GenomicFeatures, BackendData_ChromatinStates, vcfMetaData, vcfPATH)
{
#########################
### Helpers functions ###
#########################
GetVariantsInWindow <- function(file, position, genome = "hg19", type = "vcf") {
if (tolower(type) == "vcf") {
if(!missing(position)) {
if(is(position, "GRanges")) {
params <- ScanVcfParam(which = position)
} else if(position == "all") {
params <- ScanVcfParam()
} else if(is(position, "character")) {
position <- as(position, "GRanges")
params <- ScanVcfParam(which = position)
} else {
stop("I don't understand the position argument, must be unset, GRanges, or 'all'")
}
} else {
stop("without a position argument, the full vcf will be imported into memory,\n",
"this can be a very expensive operation. Set the position arg to 'all' to allow")
}
vcf <- TabixFile(file)
variants <- getFILE(file, GetVariantsInWindowVCF, params, genome, N.TRIES = 3L)
return(variants)
} else if (tolower(type) == "bed") {
if(!missing(position) & position != 'all') {
variants <- import.bed(file, which = position, genome = genome)
} else {
variants <- import.bed(file, genome = genome)
}
return(variants)
} else {
stop("type ", type, " is not yet implemented")
}
}
SetPopulation <- function(vcf, sample_sheet) {
population <- colData(vcf)
if (!("Samples" %in% colnames(population))) {
stop("vcf does not contain sample information")
}
population$Samples <- rownames(population)
sample.col <- which(grepl(pattern = "sample",
x = colnames(sample_sheet),
ignore.case = TRUE))
if (length(sample.col) > 0L) {
sample_sheet <- DataFrame(sample_sheet)
colnames(sample_sheet)[sample.col] <- "Samples"
population <- S4Vectors::merge(population, sample_sheet, all.x = TRUE, all.y = FALSE, by = "Samples")
rownames(population) <- population$Samples
colData(vcf) <- population
metadata(vcf)$source <- "funciVar"
metadata(vcf)$ld <- "none"
return(vcf)
} else {
stop("sample sheet does not contain sample column")
}
}
CalcLD <- function(vcf, index, population, return = "valid", force = TRUE) {
## check input
if(!is(vcf, "VCF")) {
stop("vcf object must be of the class VCF")
}
if (index %in% rownames(vcf)) {
if (!isSNV(vcf[rownames(vcf) %in% index, ])) {
stop("funciVar can only calculate LD for SNVs at this time")
}
} else {
stop("index snp ", index, " not found in current vcf")
}
if(missing(population)) {
population <- "ALL"
vcf.snv <- isSNV(vcf)
if (return == "valid") {
if (any(!vcf.snv)) {
vcf <- vcf[vcf.snv, ]
}
vcf <- vcf[isSNV(vcf, singleAltOnly = TRUE), ]
}
} else {
samples <- as.data.frame(colData(vcf))
samples.col <- which(samples == population, arr.ind = TRUE)
if (nrow(samples.col) > 0L) {
## metadata
samples.col <- samples.col[1, "col"]
pop.samples <- rownames(samples)[grepl(population, samples[, samples.col])]
vcf <- vcf[, pop.samples]
vcf.snv <- isSNV(vcf)
if (return == "valid") {
if (any(!vcf.snv)) {
vcf <- vcf[vcf.snv, ]
}
vcf <- vcf[isSNV(vcf, singleAltOnly = TRUE), ]
}
} else {
stop("your population was not found in your vcf.\nUse setPopulation() to add your sample descriptions, and double check that ",
population, " is present")
}
}
if(nrow(vcf) >= 1L) {
vcf.ranges <- rowRanges(vcf)[, c("REF", "ALT")]
mcols(vcf.ranges) <- c(mcols(vcf.ranges), snpSummary(vcf)[, c("a0Freq", "a1Freq", "HWEpvalue")])
colnames(mcols(vcf.ranges)) <- c("ref", "alt", "refAlleleFreq", "altAlleleFreq", "HWEpvalue")
mcols(vcf.ranges)[, "indexSNP"] <- index
mcols(vcf.ranges)[, "population"] <- population
mcols(vcf.ranges)[, "distanceToIndex"] <- abs(start(vcf.ranges[index, ]) - start(vcf.ranges))
## genotype
vcf.geno <- genotypeToSnpMatrix(vcf)$genotypes
vcf.ld <- ld(vcf.geno, vcf.geno[, index], stats = c('D.prime', 'R.squared'))
mcols(vcf.ranges)$D.prime <- vcf.ld$D.prime[, 1]
mcols(vcf.ranges)$R.squared <- vcf.ld$R.squared[, 1]
rowRanges(vcf) <- vcf.ranges
## xXx rowData(vcf) <- cbind(rowData(vcf), mcols(vcf.ranges))
if ("none" %in% metadata(vcf)$ld) {
metadata(vcf)$ld <- index
} else if(force) {
metadata(vcf)$ld <- index
} else if(!("ld" %in% names(metadata(vcf)))) {
metadata(vcf)$source <- "funciVar"
metadata(vcf)$ld <- index
} else {
stop("ld has already been calculated on this object for index snp: ", index)
}
}
return(vcf)
}
GetBioFeatures <- function(files, genome) {
if (length(files) < 1L) return(NULL)
good.files <- sapply(files, function(x) file.exists(x))
if (sum(good.files) < length(files)) {
if (sum(!good.files) <= 5L) {
stop(paste("cannot find the following", sum(!good.files), "files:\n"),
paste0(files[!good.files], "\n"))
} else {
stop(paste("cannot find", sum(!good.files), "files. Some of which are:\n"),
paste0(head(files[!good.files], n = 5L), "\n"))
}
} else {
genome <- tryCatch(Seqinfo(genome = genome), error = NULL)
bed.list <- lapply(files,
function(file, s.info) {
if (grepl(".narrowPeak", file, ignore.case = TRUE)) {
col.types <- cols_only(chr = col_character(),
start = col_integer(),
end = col_integer(),
name = col_character(),
score = col_integer(),
strand = col_character(),
signalValue = col_number())
col.names <- c("chr", "start", "end", "name", "score", "strand", "signalValue")
col.numbers <- c(1:7)
} else {
col.types <- cols_only(chr = col_character(),
start = col_integer(),
end = col_integer())
col.names <- c("chr", "start", "end")
col.numbers <- c(1:3)
}
if (any(grepl(".gz$", file))) {
xf <- suppressMessages(suppressWarnings(read_tsv(file = file, col_names = col.names,
col_types = col.types,
progress = FALSE)))
} else {
xf <- fread(input = file, sep = "\t", header = FALSE,
select = col.numbers, skip = "chr",
col.names = col.names,
encoding = "UTF-8",
stringsAsFactors = FALSE,
data.table = FALSE, showProgress = FALSE)
}
if (ncol(xf) < 7) xf$signalValue <- NA
xf <- try(GRanges(seqnames = xf$chr,
ranges = IRanges(start = xf$start + 1L,
end = xf$end),
strand = "*",
feature = base::rep.int(basename(file), nrow(xf)),
signalValue = xf$signalValue,
seqinfo = s.info))
return(xf)
}, s.info = genome)
}
if (is.null(bed.list)) {
return(GRangesList())
} else {
bed.list <- GRangesList(bed.list)
names(bed.list) <- basename(files)
return(bed.list)
}
}
GetSegmentations <- function(files) {
bed.list <- sapply(files, function(file) {
bed <- import.bed(file)
})
bed.names <- sapply(bed.list, function(bed) {
bed.name <- tryCatch(bed@trackLine@name, error = NA)
})
bed.names[is.na(bed.names)] <- files[is.na(bed.names)]
bed.list <- Map(format.bed, bed.list, bed.names)
if (is.null(bed.list)) {
return(GRangesList())
} else {
bed.list <- GRangesList(bed.list)
names(bed.list) <- bed.names
return(bed.list)
}
}
format.bed <- function(bed, name) {
bed.m <- mcols(bed)
mcols(bed) <- NULL
mcols(bed)$sample <- name
mcols(bed)$state <- bed.m$name
return(bed)
}
SplitVcfLd <- function(vcf, ld = list(metric = "R.squared", cutoff = 0.8, maf = 0.01), strict.subset = TRUE) {
if (!is(vcf, "VCF")) {
stop("parameter vcf must be a VCF object")
}
if (!all(names(ld) %in% c("metric", "cutoff", "maf"))) {
stop("parameter ld must contain the fields 'metric', 'cutoff', and 'maf'")
}
if (!(ld[["metric"]] %in% colnames(mcols(rowRanges(vcf))))) {
stop("in argument ld$metric: '", ld[['metric']],"' must be present in rowRanges(vcf)")
}
if (!(ld[["maf"]] >= 0 && ld[["maf"]] <= 0.5)) {
stop("in argument ld$maf: '", ld[['maf']], "' must be between the values of 0 and 0.5")
}
orient <- mcols(rowRanges(vcf))[, "altAlleleFreq"] < mcols(rowRanges(vcf))[, "refAlleleFreq"]
filter <- mcols(rowRanges(vcf))[, "altAlleleFreq"] >= ld[["maf"]]
filter[!orient] <- mcols(rowRanges(vcf))[, "refAlleleFreq"][!orient] >= ld[["maf"]]
fg.filter <- filter & mcols(rowRanges(vcf))[, ld[["metric"]]] >= ld[["cutoff"]]
if (strict.subset) {
bg.filter <- fg.filter | mcols(rowRanges(vcf))[, ld[["metric"]]] < ld[["cutoff"]]
} else {
bg.filter <- filter & !mcols(rowRanges(vcf))[, ld[["metric"]]] >= ld[["cutoff"]]
}
metadata(vcf)$strict.subset <- strict.subset
bg.filter <- fg.filter | mcols(rowRanges(vcf))[, ld[["metric"]]] < ld[["cutoff"]]
return(list(fg = vcf[fg.filter & !is.na(fg.filter), ], bg = vcf[bg.filter & !is.na(bg.filter), ]))
}
CalculateEnrichment <- function(variants, features, feature.type = "biofeatures",
CI = 0.95, prior = c(a = 0.5, b = 0.5),
strict.subset = "guess", return.overlaps = FALSE) {
# set strict.subset
if (strict.subset == "guess") {
test.sub <- c(metadata(variants$fg)$strict.subset, metadata(variants$bg)$strict.subset)
if (all(test.sub, !is.null(test.sub))) {
strict.subset <- TRUE
} else if (any(metadata(variants$fg)$strict.subset, metadata(variants$bg)$strict.subset)) {
stop("foreground and background disagree about whether fg is a strict subset of bg\n",
"create foreground and background with SplitVcfLD")
} else if (all(rownames(variants$fg) %in% rownames(variants$bg))) {
strict.subset <- TRUE
} else {
strict.subset <- FALSE
}
} else if (!is.logical(strict.subset)) {
stop("strict.subset must be one of 'guess', TRUE, or FALSE")
}
# features
if(!is(variants, "list")) stop("variants must be an object of class list")
if (!missing(features)) {
if (feature.type == "biofeatures") {
if(inherits(features, "GRangesList")) features <- unlist(features, use.names = FALSE)
variants$fg <- SetOverlaps(variants$fg, features)
variants$bg <- SetOverlaps(variants$bg, features)
fg.features <- colnames(mcols(ShowOverlaps(variants$fg)))
bg.features <- colnames(mcols(ShowOverlaps(variants$bg)))
all.features <- union(fg.features, bg.features)
## first fg
if (any(!(is.element(all.features, fg.features)))) {
if (is(variants$fg, "VCF")) {
mcols(rowRanges(variants$fg))[, all.features[!(is.element(all.features, fg.features))]] <- 0L
} else if (is(variants$fg, "GRanges")) {
mcols(variants$fg)[, all.features[!(is.element(all.features, fg.features))]] <- 0L
}
}
if (is(variants$bg, "VCF")) {
mcols(rowRanges(variants$fg)) <- cbind(mcols(rowRanges(variants$fg))[, 1:metadata(variants$fg)$overlap.offset-1], mcols(rowRanges(variants$fg))[, all.features, drop = FALSE])
} else if (is(variants$fg, "GRanges")) {
mcols(variants$fg) <- cbind(mcols(variants$fg)[, 1:attributes(variants$fg)$metadata$overlap.offset-1], mcols(variants$fg)[, all.features, drop = FALSE])
}
## then bg
if (any(!(is.element(all.features, bg.features)))) {
if (is(variants$bg, "VCF")) {
mcols(rowRanges(variants$bg))[, all.features[!(is.element(all.features, bg.features))]] <- 0L
} else if (is(variants$bg, "GRanges")) {
mcols(variants$bg)[, all.features[!(is.element(all.features, bg.features))]] <- 0L
}
}
if (is(variants$bg, "VCF")) {
mcols(rowRanges(variants$bg)) <- cbind(mcols(rowRanges(variants$bg))[, 1:metadata(variants$bg)$overlap.offset-1], mcols(rowRanges(variants$bg))[, all.features, drop = FALSE])
} else if (is(variants$bg, "GRanges")) {
mcols(variants$bg) <- cbind(mcols(variants$bg)[, 1:attributes(variants$bg)$metadata$overlap.offset-1], mcols(variants$bg)[, all.features, drop = FALSE])
}
}
}
# feature.type = "biofeatures"
if (feature.type == "biofeatures") {
enrichment <- enrich.features(fg = variants$fg, bg = variants$bg, CI = CI, prior = prior, strict.subset = strict.subset)
if(return.overlaps) {
if(all(names(mcols(variants$fg)) == names(mcols(variants$bg)))) {
list.fun <- GenomicRanges::GRangesList
} else {
list.fun <- S4Vectors::List
}
if (is(variants$fg, "GRanges") & is(variants$bg, "GRanges")) {
overlaps <- list.fun(foreground.overlaps = variants$fg, background.overlaps = variants$bg)
} else {
overlaps <- list.fun(foreground.overlaps = rowRanges(variants$fg), background.overlaps = rowRanges(variants$bg))
}
return(list(overlaps = overlaps, enrichment = enrichment))
} else {
return(enrichment)
}
} else if (feature.type == "segmentations") {
if (missing(features)) {
stop("include segmentations as the 'features' argument")
} else {
if (is(variants$fg, "GRanges") & is(variants$bg, "GRanges")) {
search.range <- GenomicRanges::union(range(variants$fg), range(variants$bg))
} else {
search.range <- GenomicRanges::union(range(rowRanges(variants$fg)), range(rowRanges(variants$bg)))
}
if (is(features, "GRangesList")) {
features <- unlist(features, use.names = FALSE)
}
# nfeatures <- gaps(features)
# nfeatures <- nfeatures[strand(nfeatures) == "*"]
# mcols(nfeatures)$sample <- "none"
# mcols(nfeatures)$state <- "unclassified"
# features <- c(features, nfeatures)
features <- keepSeqlevels(subsetByOverlaps(features, search.range), seqlevelsInUse(search.range))
enrichment <- enrich.segments(fg = variants$fg,
bg = variants$bg,
features = features,
CI = CI,
prior = prior,
strict.subset = strict.subset,
return.overlaps = return.overlaps)
}
} else {
stop("feature.type: ", feature.type, "is not availible; try 'biofeatures' or 'segmentations'")
}
return(enrichment)
}
SetOverlaps <- function(variants, features) {
nfeatures <- gaps(features)
nfeatures <- nfeatures[strand(nfeatures) == "*"]
if(all(names(mcols(features)) == c("sample", "state"))) {
mcols(nfeatures)$sample <- "none"
mcols(nfeatures)$state <- "unclassified"
id.name <- "sample"
} else if (all(names(mcols(features)) == c("feature", "signalValue"))) {
mcols(nfeatures)$feature <- "none"
mcols(nfeatures)$signalValue <- 0L
id.name <- "feature"
}
features <- c(features, nfeatures)
if (is.null(names(variants))) {
names(variants) <- make.unique(as.character(variants))
}
overlaps <- findOverlaps(variants, features, ignore.strand = TRUE)
overlaps <- data.frame(from = names(variants)[from(overlaps)],
to = mcols(features)[to(overlaps), id.name],
stringsAsFactors = FALSE)
resmatrix <- make.overlap.matrix(overlaps)
resmatrix <- resmatrix[names(variants), !grepl("^none$",
colnames(resmatrix)), drop = FALSE]
if(ncol(resmatrix) > 0L) {
if(is(variants, "GRanges")) {
overlap.offset <- ncol(mcols(variants)) + 1
mcols(variants) <- cbind(mcols(variants), DataFrame(resmatrix))
attributes(variants)$metadata$overlap.offset <- overlap.offset
} else if (is(variants, "VCF")) {
overlap.offset <- (ncol(mcols(rowRanges(variants))) + 1) - 4
mcols(rowRanges(variants)) <- cbind(mcols(rowRanges(variants)), DataFrame(resmatrix))
metadata(variants)$overlap.offset <- overlap.offset
} else {
stop("variants must be either a GRanges or VCF object, your object is: ", class(variants))
}
} else {
if(is(variants, "GRanges")) {
metadata(variants)$overlap.offset <- ncol(mcols(variants)) + 1
} else if (is(variants, "VCF")) {
metadata(variants)$overlap.offset <- ncol(mcols(rowRanges(variants))) + 1
} else {
stop("variants must be either a GRanges or VCF object, your object is: ", class(variants))
}
}
return(variants)
}
ShowOverlaps <- function(variants, feature = NULL) {
if (is(variants, "GRanges")) {
offset <- attributes(variants)$metadata$overlap.offset
} else if (is(variants, "VCF")) {
offset <- metadata(variants)$overlap.offset
variants <- rowRanges(variants)
variants <- variants[, 1:(ncol(mcols(variants)) - 4)]
} else {
stop("variants must be either a VCF or GRanges object")
}
if (ncol(mcols(variants)) < offset) {
return(DataFrame())
}
if (is.null(feature)) {
if (!is.null(offset)) {
return(variants[, offset:ncol(mcols(variants))])
} else {
stop("no overlap data is availible, run SetOverlaps() first")
}
} else {
if (any(feature %in% colnames(mcols(rowRanges(variants))))) {
return(variants[, feature])
} else {
stop("no overlap data is availible for feature search: ", feature)
}
}
}
PlotEnrichment <- function(variant.enrichment, value = "difference", block1 = NULL, block2 = NULL, color.by = NULL, colors = NULL, ncol = 1) {
## check args
if (!is(variant.enrichment, "data.frame"))
stop("variant.enrichment must be a data.frame")
if (!is.null(block1)) {
if (!(block1 %in% colnames(variant.enrichment)))
stop("The block1 argument must either be NULL or a column of variant.enrichment.")
}
if (!is.null(block2)) {
if (!(block2 %in% colnames(variant.enrichment)))
stop("The block2 argument must either be NULL or a column of variant.enrichment.")
}
if (!is.null(color.by)) {
if (!(color.by %in% colnames(variant.enrichment)))
stop("The color.by argument must either be NULL or a column of variant.enrichment.")
}
if (!(value %in% c("log.odds.ratio", "difference"))) {
stop("The y argument must be either 'log.odds.ratio' or 'difference'")
}
if(value == "log.odds.ratio") value.name <- "log1p(odds ratio)"
if(value == "difference") value.name <- "difference of foreground \nadbackground distributions"
if (is.null(color.by)) {
variant.enrichment$color <- "#ececec"
variant.enrichment[variant.enrichment$significant, "color"] <- "#ff0000"
color.is <- "self"
} else {
color.vals <- variant.enrichment[, color.by]
names(color.vals) <- rownames(variant.enrichment)
color.name <- color.by
if(!is.null(colors)) {
if(length(color.vals) == length(colors)) {
variant.enrichment$color <- colors
color.is <- "self"
} else {
stop("arg 'colors' is not the same length as color.by")
}
}
if (all(grepl("^#", color.vals) & (str_length(color.vals) == 7L))) {
variant.enrichment$color <- color.vals
variant.enrichment[!variant.enrichment$significant, "color"] <- alpha(variant.enrichment[!variant.enrichment$significant, "color"], 0.5)
color.is <- "self"
} else if (is.character(color.vals) | is.factor(color.vals) | is.logical(color.vals)) {
color.vals <- as.factor(color.vals)
if(nlevels(color.vals) < 10L) {
levels(color.vals) <- brewer.pal(nlevels(color.vals), "Set1")
} else if(nlevels(color.vals) < 20L) {
kelly.colours <- c("gray95", "gray13", "gold2", "plum4",
"darkorange1", "lightskyblue2", "firebrick",
"burlywood3", "gray51", "springgreen4", "lightpink2",
"deepskyblue4", "lightsalmon2", "mediumpurple4",
"orange", "maroon", "yellow3", "brown4",
"yellow4", "sienna4", "chocolate", "gray19")
levels(color.vals) <- kelly.colours[3:(nlevels(color.vals)+2)]
} else {
levels(color.vals) <- colorRampPalette(brewer.pal(9, "Spectral"))(nlevels(color.vals))
}
variant.enrichment$color <- as.character(color.vals)
variant.enrichment[!variant.enrichment$significant, "color"] <- alpha(variant.enrichment[!variant.enrichment$significant, "color"], 0.5)
color.is <- "self"
} else {
variant.enrichment$color <- variant.enrichment[, color.by]
color.is <- "cont"
}
}
ep <- ggplot(variant.enrichment, aes_string(x = "sample", y = value, group = "color")) + theme_minimal()
if (n_distinct(variant.enrichment$sample) <= 7L) {
if(value == "log.odds.ratio") {
ep <- ep + geom_crossbar(aes(ymin = log.odds.lower, ymax = log.odds.upper, color = color), fatten = 2, width = 0.8)
} else {
ep <- ep + geom_crossbar(aes(ymin = lower, ymax = upper, color = color), fatten = 2, width = 0.8)
}
} else {
if(value == "log.odds.ratio") {
ep <- ep + geom_pointrange(aes(ymin = log.odds.lower, ymax = log.odds.upper, color = color), fatten = 0.5)
} else {
ep <- ep + geom_pointrange(aes(ymin = lower, ymax = upper, color = color), fatten = 0.5)
}
}
ep <- ep + scale_x_discrete(name = "Sample")
if(value == "log.odds.ratio") {
my.max <- round(max(variant.enrichment$odds.upper), 2) + 0.01
my.min <- round(min(variant.enrichment$odds.lower), 2) - 0.01
ep <- ep + scale_y_continuous(name = value.name)
ep <- ep + geom_hline(yintercept = 0, color = "#c4c4c4", alpha = 0.5) +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=log1p(1), alpha=0.1, fill="black")
} else {
my.max <- round(max(variant.enrichment$upper), 2) + 0.01
my.min <- round(min(variant.enrichment$lower), 2) - 0.01
ep <- ep + scale_y_continuous(name = value.name, breaks = round(seq(from = my.min, to = my.max, length.out = 5), 2))
ep <- ep + geom_hline(yintercept = 0, color = "#c4c4c4", alpha = 0.5) +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=0, alpha=0.1, fill="black")
}
if (all(!is.null(block1), !is.null(block2))) {
ep <- ep + theme(axis.text.x = element_blank(),
legend.position="bottom",
panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(),
strip.text.y = element_text(angle = 0, hjust = 0, vjust = 0.5, size = rel(1.5)),
strip.text.x = element_text(angle = 0, hjust = 0.5, vjust = 1, size = rel(1.5)))
} else {
ep <- ep + theme(axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5, size = rel(1.5)),
legend.position="bottom",
panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(),
strip.text.x = element_text(angle = 0, hjust = 0.5, vjust = 1, size = rel(1.5)))
}
if(!is.null(block1) & !is.null(block2)) {
my.form <- as.formula(paste(block1, "~", block2))
ep <- ep + facet_grid(my.form, scales = "free_x", space = "free_x", switch = "x")
} else if (!is.null(block1) & is.null(block2)) {
my.form <- as.formula(paste("~", block1))
ep <- ep + facet_wrap(my.form, ncol = ncol, strip.position = ifelse(ncol > 1L, "top", "right"))
} else if (is.null(block1) & !is.null(block2)) {
my.form <- as.formula(paste("~", block2))
ep <- ep + facet_wrap(my.form, ncol = ncol, strip.position = ifelse(ncol > 1L, "top", "right"))
}
if(color.is == "cont") {
ep <- ep + scale_color_brewer(palette = "Greens")
} else {
ep <- ep + scale_color_identity()
}
plot(ep)
return(invisible(ep))
}
GetVariantsInWindowVCF <- function(file, param, genome) {
vcf <- readVcf(file = file, genome = genome, param = param)
if (nrow(vcf) < 1L) stop("no variants found in interval; \n this is sometimes an error in fetching remote file, try with a local vcf")
vcf <- keepSeqlevels(vcf, seqlevelsInUse(vcf))
seqlevelsStyle(vcf) <- "UCSC"
metadata(vcf)$overlap.offset <- NA
return(vcf)
}
getFILE <- function(FILE, FUN, ..., N.TRIES=1L) {
N.TRIES <- as.integer(N.TRIES)
stopifnot(length(N.TRIES) == 1L, !is.na(N.TRIES))
while (N.TRIES > 0L) {
result <- tryCatch(FUN(FILE, ...), error = identity)
if (!inherits(result, "error"))
break
N.TRIES <- N.TRIES - 1L
}
if (N.TRIES == 0L) {
stop("'getFILE()' failed:",
"\nFILE: ", FILE,
"\nerror: ", conditionMessage(result))
}
result
}
make.overlap.matrix <- function(overlaps) {
overlap.table <- table(overlaps)
output.matrix <- matrix(overlap.table,
nrow = nrow(overlap.table),
dimnames = list(rownames(overlap.table),
colnames(overlap.table)))
if (!is(output.matrix, "matrix")) {
dim(output.matrix) <- c(length(output.matrix), 1)
dimnames(output.matrix) <- list(rownames(overlap.table),
colnames(overlap.table))
}
output.matrix[output.matrix > 1L] <- 1L
return(output.matrix)
}
enrich.segments <- function(fg, bg, features, CI, prior, strict.subset, return.overlaps) {
if (is(features, "GRangesList")) {
features <- unlist(features, use.names = FALSE)
}
if (all(c("sample", "state") %in% colnames(mcols(features)))) {
states <- unique(mcols(features)$state)
if (length(states) > 1L) {
myapply <- pblapply
} else {
myapply <- lapply
}
enrichment <- myapply(states, function(state,
local.features = features,
local.fg = fg,
local.bg = bg,
local.CI = CI,
local.prior = prior,
local.strict.subset = strict.subset,
local.return.overlaps = return.overlaps) {
local.features <- local.features[mcols(local.features)$state %in% state, ]
local.fg <- SetOverlaps(local.fg, local.features)
local.bg <- SetOverlaps(local.bg, local.features)
## Equalize feature columns
fg.features <- colnames(mcols(ShowOverlaps(local.fg)))
bg.features <- colnames(mcols(ShowOverlaps(local.bg)))
all.features <- union(fg.features, bg.features)
## first fg
if (is(local.fg, "VCF")) {
if(any(!(is.element(all.features, fg.features)))) {
mcols(rowRanges(local.fg))[, all.features[!(is.element(all.features, fg.features))]] <- 0L
}
mcols(rowRanges(local.fg)) <- cbind(mcols(rowRanges(local.fg))[, 1:metadata(local.fg)$overlap.offset - 1],
mcols(rowRanges(local.fg))[, all.features, drop = FALSE])
} else if (is(local.fg, "GRanges")) {
if (any(!(is.element(all.features, fg.features)))) {
mcols(local.fg)[, all.features[!(is.element(all.features, fg.features))]] <- 0L
}
mcols(local.fg) <- cbind(mcols(local.fg)[, 1:attributes(local.fg)$metadata$overlap.offset - 1],
mcols(local.fg)[, all.features, drop = FALSE])
}
## then bg
if (is(local.bg, "VCF")) {
if(any(!(is.element(all.features, bg.features)))) {
mcols(rowRanges(local.bg))[, all.features[!(is.element(all.features, bg.features))]] <- 0L
}
mcols(rowRanges(local.bg)) <- cbind(mcols(rowRanges(local.bg))[, 1:metadata(local.bg)$overlap.offset - 1],
mcols(rowRanges(local.bg))[, all.features, drop = FALSE])
} else if (is(local.bg, "GRanges")) {
if (any(!(is.element(all.features, bg.features)))) {
mcols(local.bg)[, all.features[!(is.element(all.features, bg.features))]] <- 0L
}
mcols(local.bg) <- cbind(mcols(local.bg)[, 1:attributes(local.bg)$metadata$overlap.offset - 1],
mcols(local.bg)[, all.features, drop = FALSE])
}
enrich <- enrich.features(fg = local.fg,
bg = local.bg,
CI = local.CI,
prior = local.prior,
strict.subset = local.strict.subset)
if (local.return.overlaps) {
return(list(e = enrich, fg.o = ShowOverlaps(local.fg), bg.o = ShowOverlaps(local.bg)))
} else {
return(enrich)
}
})
if(return.overlaps) {
overlaps <- lapply(enrichment, function(x) {
overlaps <- GRangesList(foreground.overlaps = x$fg.o, background.overlaps = x$bg.o)
return(overlaps)
})
names(overlaps) <- states
enrichment <- lapply(enrichment, function(x) {
return(x$e)
})
}
names(enrichment) <- states
for (state in states) {
enrichment[[state]]$state <- state
}
enrichment <- do.call("rbind", enrichment)
} else {
stop("Segmentations must have mcols with fields 'sample' and 'state'")
}
if(return.overlaps) {
return(list(overlaps = overlaps, enrichment = enrichment))
} else {
return(enrichment)
}
}
enrich.features <- function(fg, bg, CI, prior, strict.subset) {
## foreground overlaps
fg.over.matrix <- as.matrix(mcols(ShowOverlaps(fg)))
rownames(fg.over.matrix) <- rownames(fg)
## background overlaps
bg.over.matrix <- as.matrix(mcols(ShowOverlaps(bg)))
rownames(bg.over.matrix) <- rownames(bg)
## start enrichment
sample.size <- dim(fg.over.matrix)[[1]]
sample.stats <- colSums(fg.over.matrix)
if (strict.subset) {
total.size <- dim(bg.over.matrix)[[1]]
total.stats <- colSums(bg.over.matrix)
} else {
total.size <- dim(bg.over.matrix)[[1]] + sample.size
total.stats <- colSums(bg.over.matrix) + sample.stats
}
if (exists("prior") & !is.null(prior)) {
a <- prior[["a"]]
b <- prior[["b"]]
} else {
stop("no argument present for prior, needed for simulation")
}
enrichment <- data.frame(sample = character(),
fg.ratio = numeric(),
bg.ratio = numeric(),
probability = numeric(),
difference = numeric(),
lower = numeric(),
upper = numeric(),
fg.success = numeric(),
fg.total = numeric(),
bg.success = numeric(),
bg.total = numeric(),
stringsAsFactors = FALSE)
CI <- (1 - CI)/2
for (my.sample in seq_along(colnames(bg.over.matrix))) {
total.success <- total.stats[my.sample]
sample.success <- sample.stats[my.sample]
n1 <- sample.size
y1 <- sample.success
n2 <- total.size - sample.size
y2 <- total.success - sample.success
# SIMULATION
I = 100000 # simulations
theta1 = rbeta(I, y1 + a, (n1 - y1) + b)
theta2 = rbeta(I, y2 + a, (n2 - y2) + b)
diff = theta1 - theta2
# OUTPUT
quantiles <- try(quantile(diff,c(CI, 0.5, 1 - CI)))
#if(inherits(quantiles, "try-error")) browser()
fisher.res <- fisher.test(matrix(c(sample.success,
total.success - sample.success,
sample.size - sample.success,
(total.size - total.success) - sample.size + sample.success),
2, 2),
conf.level = CI)
result <- data.frame(sample = colnames(bg.over.matrix)[my.sample],
fg.ratio = sample.success/sample.size,
bg.ratio = total.success/total.size,
probability = mean(theta1 > theta2),
log.odds.ratio = log1p(fisher.res$estimate),
log.odds.lower = log1p(fisher.res$conf.int[[1]]),
log.odds.upper = log1p(fisher.res$conf.int[[2]]),
difference = quantiles[2],
lower = quantiles[1],
upper = quantiles[3],
fg.success = sample.success,
fg.total = sample.size,
bg.success = total.success,
bg.total = total.size,
stringsAsFactors = FALSE)
enrichment <- rbind(enrichment, result)
}
enrichment$significant <- FALSE
enrichment[enrichment$probability > (1 - CI) | enrichment$probability < CI, "significant"] <- TRUE
return(enrichment)
}
clump_data <- function(dat, clump_kb=10000, clump_r2=0.001, clump_p1=1, clump_p2=1)
{
.Deprecated("ieugwasr::ld_clump()")
if(missing(clump_r2))
{
}
if(!is.data.frame(dat))
{
stop("Expecting data frame returned from format_data")
}
if(! "pval.exposure" %in% names(dat))
{
dat$pval.exposure <- 0.99
}
if(! "id.exposure" %in% names(dat))
{
dat$id.exposure <- random_string(1)
}
d <- data.frame(rsid=dat$SNP, pval=dat$pval.exposure, id=dat$id.exposure)
out <- ieugwasr::ld_clump(d, clump_kb=clump_kb, clump_r2=clump_r2, clump_p=clump_p1)
keep <- paste(dat$SNP, dat$id.exposure) %in% paste(out$rsid, out$id)
return(dat[keep, ])
}
ld_matrix <- function(snps, with_alleles=TRUE)
{
.Deprecated("ieugwasr::ld_matrix()")
ieugwasr::ld_matrix(variants=snps, with_alleles=with_alleles)
}
# setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
zScore <- function (cntA, totA, cntB, totB) {
#calculate
avgProportion <- (cntA + cntB) / (totA + totB)
probA <- cntA/totA
probB <- cntB/totB
SE <- sqrt(avgProportion * (1-avgProportion)*(1/totA + 1/totB))
zScore <- (probA-probB) / SE
return (zScore)
}
######################
### main functions ###
######################
#--------------------------- read inputs
#------- chromatin_states
chromatin_states <- GRanges(sample = BackendData_ChromatinStates[[4]],
seqnames = gsub('chr','',BackendData_ChromatinStates[[1]]),
ranges = IRanges(BackendData_ChromatinStates[[2]], BackendData_ChromatinStates[[3]]),
state = BackendData_ChromatinStates[[4]])
#------- BackendData_GenomicFeatures
BackendData_GenomicFeatures$seqnames <- gsub('chr','',BackendData_GenomicFeatures$seqnames)
BackendData_GenomicFeatures <- BackendData_GenomicFeatures[which(BackendData_GenomicFeatures$seqnames %in% c(1:22, 'X')),]
BackendData_GenomicFeatures <- GRanges(sample = BackendData_GenomicFeatures$sample,
seqnames = BackendData_GenomicFeatures$seqnames,
ranges = IRanges(BackendData_GenomicFeatures$start, BackendData_GenomicFeatures$end),
state = BackendData_GenomicFeatures$state)
#--------------------------------- input
#- query
indexSNP <- SNP
SNP_chr_POS <- snps.from.rsid(rsid = SNP, dbSNP = SNPlocs.Hsapiens.dbSNP144.GRCh38, search.genome = BSgenome.Hsapiens.UCSC.hg38)
SNP_chr_POS <- data.frame(SNP_chr_POS, stringsAsFactors = FALSE)
posQuerySNP <- GRanges(paste0(gsub('chr','',as.character(SNP_chr_POS$seqnames)),':',as.character(SNP_chr_POS$start),'-',as.character(SNP_chr_POS$end))) + windowSize
#Using the 1MB window that we defined for the index variant, we capture the relevant variants from the VCF file for our LD calculations.
if(!is.null(vcfPATH))
{
#---- vcf is the address of the vcf file
chr.remote.vcf <- vcfPATH
vcfsubset <- NULL
vcfsubset <- GetVariantsInWindow(file = chr.remote.vcf,position = posQuerySNP[1], type = "vcf")
my.samples <- fread(vcfMetaData, stringsAsFactors = FALSE)
vcfsubset <- SetPopulation(vcfsubset, sample_sheet = my.samples)
row.names(vcfsubset) <- make.names(row.names(vcfsubset), unique = TRUE)
}
LD <- CalcLD(vcfsubset, index = indexSNP, population = "EUR")
#----------------- foreground
vcfsubsetsnps <- SplitVcfLd(vcf = LD, ld = c(metric = "R.squared", cutoff = 1, maf = mafThreshold), strict.subset = TRUE) #a strict subset cannot be the same set, that is, it cannot contain all of the elements that the other set does. Or in other words, a strict subset must be smaller, while a subset can be the same size.
length( as.character(names(vcfsubsetsnps$fg)) )
length( as.character(names(vcfsubsetsnps$bg)) )
F1 <- as.character(names(vcfsubsetsnps$fg))
fg_variants <- snps.from.rsid(rsid = F1, dbSNP = SNPlocs.Hsapiens.dbSNP144.GRCh38, search.genome = BSgenome.Hsapiens.UCSC.hg38)
LD <- CalcLD(vcfsubset, index = indexSNP, population = "EUR")
vcfsubsetsnps <- SplitVcfLd(vcf = LD, ld = c(metric = "R.squared", cutoff = 0.001, maf = mafThreshold), strict.subset = TRUE) #a strict subset cannot be the same set, that is, it cannot contain all of the elements that the other set does. Or in other words, a strict subset must be smaller, while a subset can be the same size.
length( as.character(names(vcfsubsetsnps$fg)) )
length( as.character(names(vcfsubsetsnps$bg)) )
bg_variants <- snps.from.rsid(rsid = as.character(names(vcfsubsetsnps$bg)),
dbSNP = SNPlocs.Hsapiens.dbSNP144.GRCh38,
search.genome = BSgenome.Hsapiens.UCSC.hg38)
length(names(vcfsubsetsnps$bg))
foreground_sub <- as.data.frame(fg_variants)
foreground_sub$seqnames <- gsub('chr', '', foreground_sub$seqnames)
fGRhg38 <- GRanges(
seqnames <- Rle(as.character(foreground_sub$seqnames)),
ranges <- IRanges(start=as.numeric(foreground_sub$start), end=as.numeric(foreground_sub$end)+1, names=foreground_sub$SNP_ID),
strand <- Rle(as.character(foreground_sub$strand))
)
background_sub <- as.data.frame(bg_variants)
background_sub$seqnames <- gsub('chr', '', background_sub$seqnames)
bGRhg38 <- GRanges(
seqnames<-Rle(as.character(background_sub$seqnames)),
ranges <- IRanges(start=as.numeric(background_sub$start), end=as.numeric(background_sub$end)+1, names=background_sub$SNP_ID),
strand <- Rle(as.character(background_sub$strand))
)
#--------------------------------- CS enrichment
#--- enrich foreground
fo <- findOverlaps(query=fGRhg38, subject=chromatin_states, type="any")
foreground_CS <- chromatin_states[subjectHits(fo),]
#--- enrich background
fo <- findOverlaps(query=bGRhg38, subject=chromatin_states, type="any")
Background_CS <- chromatin_states[subjectHits(fo),]
CSs <- unique(foreground_CS$sample)
for(t in 1:length(CSs))
{
cntA<-length(which(foreground_CS$sample==CSs[t]))
totA<-length(fGRhg38)
cntB<-length(which(Background_CS$sample==CSs[t]))
totB<-length(bGRhg38)
table <- c(cntA, totA, cntB, totB)
dim(table)<-c(2,2)
fishert <- fisher.test(table)
z_score <- zScore(cntA, totA, cntB, totB)
temp <- data.frame(SNP = indexSNP, CS = CSs[t], p = fishert$p.value, oddsratio = fishert$estimate, CIl= fishert$conf.int[1], CIh = fishert$conf.int[2], zScore = z_score, FE = cntA, FA = totA, BE = cntB, BA = totB)
if(t==1){RESULTsChromatinState = temp}else{RESULTsChromatinState = rbind(RESULTsChromatinState, temp)}
print(z_score)
}
#------------------- genomic features
#--- enrich foreground
fo <- findOverlaps(query=fGRhg38, subject=BackendData_GenomicFeatures, type="any")
foreground_GR <- BackendData_GenomicFeatures[subjectHits(fo),]
#--- enrich background
fo <- findOverlaps(query=bGRhg38, subject=BackendData_GenomicFeatures, type="any")
Background_GR <- BackendData_GenomicFeatures[subjectHits(fo),]
if(length(foreground_GR)>0)
{
GRs <- unique(foreground_GR$sample)
for(t in 1:length(GRs))
{
cntA<-length(which(foreground_GR$sample==GRs[t]))
totA<-length(foreground_GR)
cntB<-length(which(Background_GR$sample==GRs[t]))
totB<-length(Background_GR)
table <- c(cntA, totA, cntB, totB)
dim(table)<-c(2,2)
fishert <- fisher.test(table)
z_score <- zScore(cntA, totA, cntB, totB)
temp <- data.frame(SNP = indexSNP, GR = GRs[t], p = fishert$p.value, oddsratio = fishert$estimate, CIl= fishert$conf.int[1], CIh = fishert$conf.int[2], zScore = z_score, FE = cntA, FA = totA, BE = cntB, BA = totB)
if(t==1){RESULTsGenomicFeatures = temp}else{RESULTsGenomicFeatures = rbind(RESULTsGenomicFeatures, temp)}
print(z_score)
}
}
#------------------- save outputs
Destiny_Folder <- system.file(package = "FEVV")
Destiny_Folder <- paste(Destiny_Folder, "/RESULTsGenomicFeatures.txt", sep = "")
write.table(
RESULTsGenomicFeatures, Destiny_Folder, sep = "\t", row.names = FALSE, quote = FALSE
)
Destiny_Folder <- system.file(package = "FEVV")
Destiny_Folder <- paste(Destiny_Folder, "/RESULTsChromatinState.txt", sep = "")
write.table(
RESULTsChromatinState, Destiny_Folder, sep = "\t", row.names = FALSE, quote = FALSE
)
print("You can find the results at: ")
print(system.file(package="FEVV"))
}
isarnassiri/FEVV documentation built on Dec. 20, 2021, 8 p.m.
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Defines functions querySNPsEnrichmentAnalysis eSNPsEnrichmentAnalysis
Documented in eSNPsEnrichmentAnalysis querySNPsEnrichmentAnalysis
#############################################################################################
###################################### eSNP enrichment ######################################
#############################################################################################
#'@export
#'@import GenomicRanges
#'@import data.table
#'@export
#'@name eSNPsEnrichmentAnalysis
#'@title Functional Enrichment of eQTL SNPs
#'@description eQTL SNPs as input: for eSNP enrichment analysis, first, for each gene/transcript, we selected associated eSNPs as foreground (F) and SNPs within the 1Mb window around the TSS as background (B) SNP sets. We used the number of overlaps of foreground (f) and background (b) SNP sets in the genomic feature or chromatin state and calculate the enrichment score (z-score and odds ratio).
#'@author {Isar Nassiri, Benjamin Fairfax}
#'@param eQTL
#'eQTL profile including the following headers: seqnames, SNP_POS, SNP_POS, SNP_ID, gene_id
#'@param TranscriptName
#'Gene or isoform Ensembl ID
#'@param windowSize
#'window around the TSS (e.g. 1000000)
#'@param FDRthreshold
#'FDR threshold (e.g. 0.001)
#'@param BackendData_GenomicFeatures
#'fifteen-core chromatin states from https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/download/
#'@param BackendData_ChromatinStates
#'10 genomic features from the biomaRt (Ensembl) and UCSC
#'@param SNPs
#'List of all SNPs in human genome
#'@return You can find the results in R object under title of 'RESULTsGenomicFeatures' and 'RESULTsChromatinState'.
#'@examples
#'data(eQTL)
#'data(BackendData_GenomicFeatures)
#'data(BackendData_ChromatinStates)
#'data(SNPs)
#'eSNPsEnrichmentAnalysis(eQTL, TranscriptName = 'ENSG00000168310', windowSize=1000000, FDRthreshold = 0.001, BackendData_GenomicFeatures, BackendData_ChromatinStates, SNPs)
#'@export
eSNPsEnrichmentAnalysis <- NULL
eSNPsEnrichmentAnalysis <- function(eQTL, TranscriptName, windowSize, FDRthreshold, BackendData_GenomicFeatures, BackendData_ChromatinStates, SNPs)
{
zScore <- function (cntA, totA, cntB, totB) {
#calculate
avgProportion <- (cntA + cntB) / (totA + totB)
probA <- cntA/totA
probB <- cntB/totB
SE <- sqrt(avgProportion * (1-avgProportion)*(1/totA + 1/totB))
zScore <- (probA-probB) / SE
return (zScore)
}
#--------------------------- inputs
states_bed <- BackendData_ChromatinStates
chromatin_states <- GRanges(sample = states_bed[[4]],
seqnames = gsub('chr','',states_bed[[1]]),
ranges = IRanges(states_bed[[2]], states_bed[[3]]),
state = states_bed[[4]])
genomic_regions <- BackendData_GenomicFeatures
genomic_regions$seqnames <- gsub('chr','',genomic_regions$seqnames)
genomic_regions <- genomic_regions[which(genomic_regions$seqnames %in% c(1:22, 'X')),]
genomic_regions <- GRanges(sample = genomic_regions$sample,
seqnames = genomic_regions$seqnames,
ranges = IRanges(genomic_regions$start, genomic_regions$end),
state = genomic_regions$state)
SNPs_Imputed <- SNPs
SNPs_Imputed_subject <- GRanges(Rle(as.character(SNPs_Imputed$CHROM)), IRanges(SNPs_Imputed$POS, width=2), rsID = SNPs_Imputed$ID)
SNPs_Imputed <- as.data.frame(SNPs_Imputed)
eQTL <- eQTL[eQTL$FDR < FDRthreshold,]
eQTL <- eQTL[!is.na(eQTL$seqnames),]
eQTL <- as.data.frame(eQTL)
#--- select eQTLs
eQTL_sub <- eQTL[which(eQTL$gene_id == TranscriptName),]
dim(eQTL_sub)
#--- Foreground_rsIDs
Foreground_rsIDs <- GRanges(paste0(eQTL_sub$seqnames,':',eQTL_sub$SNP_POS,'-',eQTL_sub$SNP_POS), rsID = eQTL_sub$SNP_ID)
length(Foreground_rsIDs)
#--- Background_rsIDs - select SNPs any a 1Mb windows
posQuerySNP <- GRanges(paste0(eQTL_sub$seqnames[1],':',eQTL_sub$start[1],'-',eQTL_sub$start[1])) + windowSize
fo <- findOverlaps(query=posQuerySNP, subject=SNPs_Imputed_subject, type="any")
Background_rsIDs <- SNPs_Imputed_subject[subjectHits(fo),]
#--- remove foreground from background
Background_rsIDs <- Background_rsIDs[-which(Background_rsIDs$rsID %in% Foreground_rsIDs$rsID),]
length(Background_rsIDs)
#------------------- chromatin state
#--- enrich foreground
fo <- findOverlaps(query=Foreground_rsIDs, subject=chromatin_states, type="any")
foreground_CS <- chromatin_states[subjectHits(fo),]
#--- enrich background
fo <- findOverlaps(query=Background_rsIDs, subject=chromatin_states, type="any")
Background_CS <- chromatin_states[subjectHits(fo),]
CSs <- unique(foreground_CS$sample)
for(t in 1:length(CSs))
{
cntA<-length(which(foreground_CS$sample==CSs[t]))
totA<-length(Foreground_rsIDs)
cntB<-length(which(Background_CS$sample==CSs[t]))
totB<-length(Background_rsIDs)
table <- c(cntA, totA, cntB, totB)
dim(table)<-c(2,2)
fishert <- fisher.test(table)
z_score <- zScore(cntA, totA, cntB, totB)
temp <- data.frame(gene = TranscriptName, CS = CSs[t], p = fishert$p.value, oddsratio = fishert$estimate, CIl= fishert$conf.int[1], CIh = fishert$conf.int[2], zScore = z_score, FE = cntA, FA = totA, BE = cntB, BA = totB)
if(t==1){RESULTsChromatinState = temp}else{RESULTsChromatinState = rbind(RESULTsChromatinState, temp)}
}
#------------------- genomic features
#--- enrich foreground
fo <- findOverlaps(query=Foreground_rsIDs, subject=genomic_regions, type="any")
foreground_GR = genomic_regions[subjectHits(fo),]
#--- enrich background
fo <- findOverlaps(query=Background_rsIDs, subject=genomic_regions, type="any")
Background_GR = genomic_regions[subjectHits(fo),]
if(length(foreground_GR)>0)
{
GRs <- unique(foreground_GR$sample)
for(t in 1:length(GRs))
{
cntA<-length(which(foreground_GR$sample==GRs[t]))
totA<-length(foreground_GR)
cntB<-length(which(Background_GR$sample==GRs[t]))
totB<-length(Background_GR)
table <- c(cntA, totA, cntB, totB)
dim(table)<-c(2,2)
fishert <- fisher.test(table)
z_score <- zScore(cntA, totA, cntB, totB)
temp <- data.frame(gene = TranscriptName, GR = GRs[t], p = fishert$p.value, oddsratio = fishert$estimate, CIl= fishert$conf.int[1], CIh = fishert$conf.int[2], zScore = z_score, FE = cntA, FA = totA, BE = cntB, BA = totB)
if(t==1){RESULTsGenomicFeatures = temp}else{RESULTsGenomicFeatures = rbind(RESULTsGenomicFeatures, temp)}
}
}
#------------------- save outputs
Destiny_Folder <- system.file(package = "FEVV")
Destiny_Folder <- paste(Destiny_Folder, "/RESULTsGenomicFeatures.txt", sep = "")
write.table(
RESULTsGenomicFeatures, Destiny_Folder, sep = "\t", row.names = FALSE, quote = FALSE
)
Destiny_Folder <- system.file(package = "FEVV")
Destiny_Folder <- paste(Destiny_Folder, "/RESULTsChromatinState.txt", sep = "")
write.table(
RESULTsChromatinState, Destiny_Folder, sep = "\t", row.names = FALSE, quote = FALSE
)
print("You can find the results at: ")
print(system.file(package="FEVV"))
}
#########################################################################################################
###################################### single query SNP enrichment ######################################
#########################################################################################################
#'@export
#'@import SNPlocs.Hsapiens.dbSNP144.GRCh38
#'@import BSgenome.Hsapiens.UCSC.hg38
#'@import motifbreakR
#'@import snpStats
#'@import GenomicRanges
#'@import data.table
#'@import biomaRt
#'@import GenomicRanges
#'@import VariantAnnotation
#'@name querySNPsEnrichmentAnalysis
#'@title Single query SNP enrichment
#'@description a query SNP as the input: a query SNP as input: first, the 1MB window is defined for the query SNP, and the relevant variants captured from the 1000 genomes data in the European population background. Next, we split the SNPs based upon a LD (R?? = 0.8 and MAF = 0.01) to foreground (query SNP and its LD proxies) and background SNPs sets. We used the number of overlaps of foreground (f) and background (b) SNP sets in the genomic feature or chromatin state and calculate the enrichment score (z-score and odds ratio).
#'@author {Isar Nassiri, Benjamin Fairfax}
#'@param windowSize
#'window around the TSS (e.g. 1000000)
#'@param BackendData_GenomicFeatures
#'fifteen-core chromatin states from https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/download/
#'@param BackendData_ChromatinStates
#'10 genomic features from the biomaRt (Ensembl) and UCSC
#'@param SNP
#'a query SNP
#'@param mafThreshold
#'minor allele frequency (MAF) threshold
#'@param vcfMetaData
#'meta data of 1000 Genomes project (phase3) including 'sample', 'pop', 'super_pop', and 'gender' header
#'@param vcfPATH
#'Pathe to the vcf file.
#'@return You can find the results in R object under title of 'RESULTsGenomicFeatures' and 'RESULTsChromatinState'.
#'@examples
#'data(BackendData_GenomicFeatures)
#'data(BackendData_ChromatinStates)
#'Destiny_Folder <- system.file(package = "FEVV")
#'querySNPsEnrichmentAnalysis(SNP = 'rs13149699', mafThreshold = 0.039, windowSize = 1000000, BackendData_GenomicFeatures, BackendData_ChromatinStates, vcfMetaData = system.file("extdata", "Genotyping1000_samples_metatadata.txt", package="FEVV"), vcfPATH = 'http://hgdownload.cse.ucsc.edu/gbdb/hg19/1000Genomes/phase3/ALL.chr4.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz' )
#'@export
querySNPsEnrichmentAnalysis <- NULL
querySNPsEnrichmentAnalysis <- function(SNP, mafThreshold, windowSize, BackendData_GenomicFeatures, BackendData_ChromatinStates, vcfMetaData, vcfPATH)
{
#########################
### Helpers functions ###
#########################
GetVariantsInWindow <- function(file, position, genome = "hg19", type = "vcf") {
if (tolower(type) == "vcf") {
if(!missing(position)) {
if(is(position, "GRanges")) {
params <- ScanVcfParam(which = position)
} else if(position == "all") {
params <- ScanVcfParam()
} else if(is(position, "character")) {
position <- as(position, "GRanges")
params <- ScanVcfParam(which = position)
} else {
stop("I don't understand the position argument, must be unset, GRanges, or 'all'")
}
} else {
stop("without a position argument, the full vcf will be imported into memory,\n",
"this can be a very expensive operation. Set the position arg to 'all' to allow")
}
vcf <- TabixFile(file)
variants <- getFILE(file, GetVariantsInWindowVCF, params, genome, N.TRIES = 3L)
return(variants)
} else if (tolower(type) == "bed") {
if(!missing(position) & position != 'all') {
variants <- import.bed(file, which = position, genome = genome)
} else {
variants <- import.bed(file, genome = genome)
}
return(variants)
} else {
stop("type ", type, " is not yet implemented")
}
}
SetPopulation <- function(vcf, sample_sheet) {
population <- colData(vcf)
if (!("Samples" %in% colnames(population))) {
stop("vcf does not contain sample information")
}
population$Samples <- rownames(population)
sample.col <- which(grepl(pattern = "sample",
x = colnames(sample_sheet),
ignore.case = TRUE))
if (length(sample.col) > 0L) {
sample_sheet <- DataFrame(sample_sheet)
colnames(sample_sheet)[sample.col] <- "Samples"
population <- S4Vectors::merge(population, sample_sheet, all.x = TRUE, all.y = FALSE, by = "Samples")
rownames(population) <- population$Samples
colData(vcf) <- population
metadata(vcf)$source <- "funciVar"
metadata(vcf)$ld <- "none"
return(vcf)
} else {
stop("sample sheet does not contain sample column")
}
}
CalcLD <- function(vcf, index, population, return = "valid", force = TRUE) {
## check input
if(!is(vcf, "VCF")) {
stop("vcf object must be of the class VCF")
}
if (index %in% rownames(vcf)) {
if (!isSNV(vcf[rownames(vcf) %in% index, ])) {
stop("funciVar can only calculate LD for SNVs at this time")
}
} else {
stop("index snp ", index, " not found in current vcf")
}
if(missing(population)) {
population <- "ALL"
vcf.snv <- isSNV(vcf)
if (return == "valid") {
if (any(!vcf.snv)) {
vcf <- vcf[vcf.snv, ]
}
vcf <- vcf[isSNV(vcf, singleAltOnly = TRUE), ]
}
} else {
samples <- as.data.frame(colData(vcf))
samples.col <- which(samples == population, arr.ind = TRUE)
if (nrow(samples.col) > 0L) {
## metadata
samples.col <- samples.col[1, "col"]
pop.samples <- rownames(samples)[grepl(population, samples[, samples.col])]
vcf <- vcf[, pop.samples]
vcf.snv <- isSNV(vcf)
if (return == "valid") {
if (any(!vcf.snv)) {
vcf <- vcf[vcf.snv, ]
}
vcf <- vcf[isSNV(vcf, singleAltOnly = TRUE), ]
}
} else {
stop("your population was not found in your vcf.\nUse setPopulation() to add your sample descriptions, and double check that ",
population, " is present")
}
}
if(nrow(vcf) >= 1L) {
vcf.ranges <- rowRanges(vcf)[, c("REF", "ALT")]
mcols(vcf.ranges) <- c(mcols(vcf.ranges), snpSummary(vcf)[, c("a0Freq", "a1Freq", "HWEpvalue")])
colnames(mcols(vcf.ranges)) <- c("ref", "alt", "refAlleleFreq", "altAlleleFreq", "HWEpvalue")
mcols(vcf.ranges)[, "indexSNP"] <- index
mcols(vcf.ranges)[, "population"] <- population
mcols(vcf.ranges)[, "distanceToIndex"] <- abs(start(vcf.ranges[index, ]) - start(vcf.ranges))
## genotype
vcf.geno <- genotypeToSnpMatrix(vcf)$genotypes
vcf.ld <- ld(vcf.geno, vcf.geno[, index], stats = c('D.prime', 'R.squared'))
mcols(vcf.ranges)$D.prime <- vcf.ld$D.prime[, 1]
mcols(vcf.ranges)$R.squared <- vcf.ld$R.squared[, 1]
rowRanges(vcf) <- vcf.ranges
## xXx rowData(vcf) <- cbind(rowData(vcf), mcols(vcf.ranges))
if ("none" %in% metadata(vcf)$ld) {
metadata(vcf)$ld <- index
} else if(force) {
metadata(vcf)$ld <- index
} else if(!("ld" %in% names(metadata(vcf)))) {
metadata(vcf)$source <- "funciVar"
metadata(vcf)$ld <- index
} else {
stop("ld has already been calculated on this object for index snp: ", index)
}
}
return(vcf)
}
GetBioFeatures <- function(files, genome) {
if (length(files) < 1L) return(NULL)
good.files <- sapply(files, function(x) file.exists(x))
if (sum(good.files) < length(files)) {
if (sum(!good.files) <= 5L) {
stop(paste("cannot find the following", sum(!good.files), "files:\n"),
paste0(files[!good.files], "\n"))
} else {
stop(paste("cannot find", sum(!good.files), "files. Some of which are:\n"),
paste0(head(files[!good.files], n = 5L), "\n"))
}
} else {
genome <- tryCatch(Seqinfo(genome = genome), error = NULL)
bed.list <- lapply(files,
function(file, s.info) {
if (grepl(".narrowPeak", file, ignore.case = TRUE)) {
col.types <- cols_only(chr = col_character(),
start = col_integer(),
end = col_integer(),
name = col_character(),
score = col_integer(),
strand = col_character(),
signalValue = col_number())
col.names <- c("chr", "start", "end", "name", "score", "strand", "signalValue")
col.numbers <- c(1:7)
} else {
col.types <- cols_only(chr = col_character(),
start = col_integer(),
end = col_integer())
col.names <- c("chr", "start", "end")
col.numbers <- c(1:3)
}
if (any(grepl(".gz$", file))) {
xf <- suppressMessages(suppressWarnings(read_tsv(file = file, col_names = col.names,
col_types = col.types,
progress = FALSE)))
} else {
xf <- fread(input = file, sep = "\t", header = FALSE,
select = col.numbers, skip = "chr",
col.names = col.names,
encoding = "UTF-8",
stringsAsFactors = FALSE,
data.table = FALSE, showProgress = FALSE)
}
if (ncol(xf) < 7) xf$signalValue <- NA
xf <- try(GRanges(seqnames = xf$chr,
ranges = IRanges(start = xf$start + 1L,
end = xf$end),
strand = "*",
feature = base::rep.int(basename(file), nrow(xf)),
signalValue = xf$signalValue,
seqinfo = s.info))
return(xf)
}, s.info = genome)
}
if (is.null(bed.list)) {
return(GRangesList())
} else {
bed.list <- GRangesList(bed.list)
names(bed.list) <- basename(files)
return(bed.list)
}
}
GetSegmentations <- function(files) {
bed.list <- sapply(files, function(file) {
bed <- import.bed(file)
})
bed.names <- sapply(bed.list, function(bed) {
bed.name <- tryCatch(bed@trackLine@name, error = NA)
})
bed.names[is.na(bed.names)] <- files[is.na(bed.names)]
bed.list <- Map(format.bed, bed.list, bed.names)
if (is.null(bed.list)) {
return(GRangesList())
} else {
bed.list <- GRangesList(bed.list)
names(bed.list) <- bed.names
return(bed.list)
}
}
format.bed <- function(bed, name) {
bed.m <- mcols(bed)
mcols(bed) <- NULL
mcols(bed)$sample <- name
mcols(bed)$state <- bed.m$name
return(bed)
}
SplitVcfLd <- function(vcf, ld = list(metric = "R.squared", cutoff = 0.8, maf = 0.01), strict.subset = TRUE) {
if (!is(vcf, "VCF")) {
stop("parameter vcf must be a VCF object")
}
if (!all(names(ld) %in% c("metric", "cutoff", "maf"))) {
stop("parameter ld must contain the fields 'metric', 'cutoff', and 'maf'")
}
if (!(ld[["metric"]] %in% colnames(mcols(rowRanges(vcf))))) {
stop("in argument ld$metric: '", ld[['metric']],"' must be present in rowRanges(vcf)")
}
if (!(ld[["maf"]] >= 0 && ld[["maf"]] <= 0.5)) {
stop("in argument ld$maf: '", ld[['maf']], "' must be between the values of 0 and 0.5")
}
orient <- mcols(rowRanges(vcf))[, "altAlleleFreq"] < mcols(rowRanges(vcf))[, "refAlleleFreq"]
filter <- mcols(rowRanges(vcf))[, "altAlleleFreq"] >= ld[["maf"]]
filter[!orient] <- mcols(rowRanges(vcf))[, "refAlleleFreq"][!orient] >= ld[["maf"]]
fg.filter <- filter & mcols(rowRanges(vcf))[, ld[["metric"]]] >= ld[["cutoff"]]
if (strict.subset) {
bg.filter <- fg.filter | mcols(rowRanges(vcf))[, ld[["metric"]]] < ld[["cutoff"]]
} else {
bg.filter <- filter & !mcols(rowRanges(vcf))[, ld[["metric"]]] >= ld[["cutoff"]]
}
metadata(vcf)$strict.subset <- strict.subset
bg.filter <- fg.filter | mcols(rowRanges(vcf))[, ld[["metric"]]] < ld[["cutoff"]]
return(list(fg = vcf[fg.filter & !is.na(fg.filter), ], bg = vcf[bg.filter & !is.na(bg.filter), ]))
}
CalculateEnrichment <- function(variants, features, feature.type = "biofeatures",
CI = 0.95, prior = c(a = 0.5, b = 0.5),
strict.subset = "guess", return.overlaps = FALSE) {
# set strict.subset
if (strict.subset == "guess") {
test.sub <- c(metadata(variants$fg)$strict.subset, metadata(variants$bg)$strict.subset)
if (all(test.sub, !is.null(test.sub))) {
strict.subset <- TRUE
} else if (any(metadata(variants$fg)$strict.subset, metadata(variants$bg)$strict.subset)) {
stop("foreground and background disagree about whether fg is a strict subset of bg\n",
"create foreground and background with SplitVcfLD")
} else if (all(rownames(variants$fg) %in% rownames(variants$bg))) {
strict.subset <- TRUE
} else {
strict.subset <- FALSE
}
} else if (!is.logical(strict.subset)) {
stop("strict.subset must be one of 'guess', TRUE, or FALSE")
}
# features
if(!is(variants, "list")) stop("variants must be an object of class list")
if (!missing(features)) {
if (feature.type == "biofeatures") {
if(inherits(features, "GRangesList")) features <- unlist(features, use.names = FALSE)
variants$fg <- SetOverlaps(variants$fg, features)
variants$bg <- SetOverlaps(variants$bg, features)
fg.features <- colnames(mcols(ShowOverlaps(variants$fg)))
bg.features <- colnames(mcols(ShowOverlaps(variants$bg)))
all.features <- union(fg.features, bg.features)
## first fg
if (any(!(is.element(all.features, fg.features)))) {
if (is(variants$fg, "VCF")) {
mcols(rowRanges(variants$fg))[, all.features[!(is.element(all.features, fg.features))]] <- 0L
} else if (is(variants$fg, "GRanges")) {
mcols(variants$fg)[, all.features[!(is.element(all.features, fg.features))]] <- 0L
}
}
if (is(variants$bg, "VCF")) {
mcols(rowRanges(variants$fg)) <- cbind(mcols(rowRanges(variants$fg))[, 1:metadata(variants$fg)$overlap.offset-1], mcols(rowRanges(variants$fg))[, all.features, drop = FALSE])
} else if (is(variants$fg, "GRanges")) {
mcols(variants$fg) <- cbind(mcols(variants$fg)[, 1:attributes(variants$fg)$metadata$overlap.offset-1], mcols(variants$fg)[, all.features, drop = FALSE])
}
## then bg
if (any(!(is.element(all.features, bg.features)))) {
if (is(variants$bg, "VCF")) {
mcols(rowRanges(variants$bg))[, all.features[!(is.element(all.features, bg.features))]] <- 0L
} else if (is(variants$bg, "GRanges")) {
mcols(variants$bg)[, all.features[!(is.element(all.features, bg.features))]] <- 0L
}
}
if (is(variants$bg, "VCF")) {
mcols(rowRanges(variants$bg)) <- cbind(mcols(rowRanges(variants$bg))[, 1:metadata(variants$bg)$overlap.offset-1], mcols(rowRanges(variants$bg))[, all.features, drop = FALSE])
} else if (is(variants$bg, "GRanges")) {
mcols(variants$bg) <- cbind(mcols(variants$bg)[, 1:attributes(variants$bg)$metadata$overlap.offset-1], mcols(variants$bg)[, all.features, drop = FALSE])
}
}
}
# feature.type = "biofeatures"
if (feature.type == "biofeatures") {
enrichment <- enrich.features(fg = variants$fg, bg = variants$bg, CI = CI, prior = prior, strict.subset = strict.subset)
if(return.overlaps) {
if(all(names(mcols(variants$fg)) == names(mcols(variants$bg)))) {
list.fun <- GenomicRanges::GRangesList
} else {
list.fun <- S4Vectors::List
}
if (is(variants$fg, "GRanges") & is(variants$bg, "GRanges")) {
overlaps <- list.fun(foreground.overlaps = variants$fg, background.overlaps = variants$bg)
} else {
overlaps <- list.fun(foreground.overlaps = rowRanges(variants$fg), background.overlaps = rowRanges(variants$bg))
}
return(list(overlaps = overlaps, enrichment = enrichment))
} else {
return(enrichment)
}
} else if (feature.type == "segmentations") {
if (missing(features)) {
stop("include segmentations as the 'features' argument")
} else {
if (is(variants$fg, "GRanges") & is(variants$bg, "GRanges")) {
search.range <- GenomicRanges::union(range(variants$fg), range(variants$bg))
} else {
search.range <- GenomicRanges::union(range(rowRanges(variants$fg)), range(rowRanges(variants$bg)))
}
if (is(features, "GRangesList")) {
features <- unlist(features, use.names = FALSE)
}
# nfeatures <- gaps(features)
# nfeatures <- nfeatures[strand(nfeatures) == "*"]
# mcols(nfeatures)$sample <- "none"
# mcols(nfeatures)$state <- "unclassified"
# features <- c(features, nfeatures)
features <- keepSeqlevels(subsetByOverlaps(features, search.range), seqlevelsInUse(search.range))
enrichment <- enrich.segments(fg = variants$fg,
bg = variants$bg,
features = features,
CI = CI,
prior = prior,
strict.subset = strict.subset,
return.overlaps = return.overlaps)
}
} else {
stop("feature.type: ", feature.type, "is not availible; try 'biofeatures' or 'segmentations'")
}
return(enrichment)
}
SetOverlaps <- function(variants, features) {
nfeatures <- gaps(features)
nfeatures <- nfeatures[strand(nfeatures) == "*"]
if(all(names(mcols(features)) == c("sample", "state"))) {
mcols(nfeatures)$sample <- "none"
mcols(nfeatures)$state <- "unclassified"
id.name <- "sample"
} else if (all(names(mcols(features)) == c("feature", "signalValue"))) {
mcols(nfeatures)$feature <- "none"
mcols(nfeatures)$signalValue <- 0L
id.name <- "feature"
}
features <- c(features, nfeatures)
if (is.null(names(variants))) {
names(variants) <- make.unique(as.character(variants))
}
overlaps <- findOverlaps(variants, features, ignore.strand = TRUE)
overlaps <- data.frame(from = names(variants)[from(overlaps)],
to = mcols(features)[to(overlaps), id.name],
stringsAsFactors = FALSE)
resmatrix <- make.overlap.matrix(overlaps)
resmatrix <- resmatrix[names(variants), !grepl("^none$",
colnames(resmatrix)), drop = FALSE]
if(ncol(resmatrix) > 0L) {
if(is(variants, "GRanges")) {
overlap.offset <- ncol(mcols(variants)) + 1
mcols(variants) <- cbind(mcols(variants), DataFrame(resmatrix))
attributes(variants)$metadata$overlap.offset <- overlap.offset
} else if (is(variants, "VCF")) {
overlap.offset <- (ncol(mcols(rowRanges(variants))) + 1) - 4
mcols(rowRanges(variants)) <- cbind(mcols(rowRanges(variants)), DataFrame(resmatrix))
metadata(variants)$overlap.offset <- overlap.offset
} else {
stop("variants must be either a GRanges or VCF object, your object is: ", class(variants))
}
} else {
if(is(variants, "GRanges")) {
metadata(variants)$overlap.offset <- ncol(mcols(variants)) + 1
} else if (is(variants, "VCF")) {
metadata(variants)$overlap.offset <- ncol(mcols(rowRanges(variants))) + 1
} else {
stop("variants must be either a GRanges or VCF object, your object is: ", class(variants))
}
}
return(variants)
}
ShowOverlaps <- function(variants, feature = NULL) {
if (is(variants, "GRanges")) {
offset <- attributes(variants)$metadata$overlap.offset
} else if (is(variants, "VCF")) {
offset <- metadata(variants)$overlap.offset
variants <- rowRanges(variants)
variants <- variants[, 1:(ncol(mcols(variants)) - 4)]
} else {
stop("variants must be either a VCF or GRanges object")
}
if (ncol(mcols(variants)) < offset) {
return(DataFrame())
}
if (is.null(feature)) {
if (!is.null(offset)) {
return(variants[, offset:ncol(mcols(variants))])
} else {
stop("no overlap data is availible, run SetOverlaps() first")
}
} else {
if (any(feature %in% colnames(mcols(rowRanges(variants))))) {
return(variants[, feature])
} else {
stop("no overlap data is availible for feature search: ", feature)
}
}
}
PlotEnrichment <- function(variant.enrichment, value = "difference", block1 = NULL, block2 = NULL, color.by = NULL, colors = NULL, ncol = 1) {
## check args
if (!is(variant.enrichment, "data.frame"))
stop("variant.enrichment must be a data.frame")
if (!is.null(block1)) {
if (!(block1 %in% colnames(variant.enrichment)))
stop("The block1 argument must either be NULL or a column of variant.enrichment.")
}
if (!is.null(block2)) {
if (!(block2 %in% colnames(variant.enrichment)))
stop("The block2 argument must either be NULL or a column of variant.enrichment.")
}
if (!is.null(color.by)) {
if (!(color.by %in% colnames(variant.enrichment)))
stop("The color.by argument must either be NULL or a column of variant.enrichment.")
}
if (!(value %in% c("log.odds.ratio", "difference"))) {
stop("The y argument must be either 'log.odds.ratio' or 'difference'")
}
if(value == "log.odds.ratio") value.name <- "log1p(odds ratio)"
if(value == "difference") value.name <- "difference of foreground \nadbackground distributions"
if (is.null(color.by)) {
variant.enrichment$color <- "#ececec"
variant.enrichment[variant.enrichment$significant, "color"] <- "#ff0000"
color.is <- "self"
} else {
color.vals <- variant.enrichment[, color.by]
names(color.vals) <- rownames(variant.enrichment)
color.name <- color.by
if(!is.null(colors)) {
if(length(color.vals) == length(colors)) {
variant.enrichment$color <- colors
color.is <- "self"
} else {
stop("arg 'colors' is not the same length as color.by")
}
}
if (all(grepl("^#", color.vals) & (str_length(color.vals) == 7L))) {
variant.enrichment$color <- color.vals
variant.enrichment[!variant.enrichment$significant, "color"] <- alpha(variant.enrichment[!variant.enrichment$significant, "color"], 0.5)
color.is <- "self"
} else if (is.character(color.vals) | is.factor(color.vals) | is.logical(color.vals)) {
color.vals <- as.factor(color.vals)
if(nlevels(color.vals) < 10L) {
levels(color.vals) <- brewer.pal(nlevels(color.vals), "Set1")
} else if(nlevels(color.vals) < 20L) {
kelly.colours <- c("gray95", "gray13", "gold2", "plum4",
"darkorange1", "lightskyblue2", "firebrick",
"burlywood3", "gray51", "springgreen4", "lightpink2",
"deepskyblue4", "lightsalmon2", "mediumpurple4",
"orange", "maroon", "yellow3", "brown4",
"yellow4", "sienna4", "chocolate", "gray19")
levels(color.vals) <- kelly.colours[3:(nlevels(color.vals)+2)]
} else {
levels(color.vals) <- colorRampPalette(brewer.pal(9, "Spectral"))(nlevels(color.vals))
}
variant.enrichment$color <- as.character(color.vals)
variant.enrichment[!variant.enrichment$significant, "color"] <- alpha(variant.enrichment[!variant.enrichment$significant, "color"], 0.5)
color.is <- "self"
} else {
variant.enrichment$color <- variant.enrichment[, color.by]
color.is <- "cont"
}
}
ep <- ggplot(variant.enrichment, aes_string(x = "sample", y = value, group = "color")) + theme_minimal()
if (n_distinct(variant.enrichment$sample) <= 7L) {
if(value == "log.odds.ratio") {
ep <- ep + geom_crossbar(aes(ymin = log.odds.lower, ymax = log.odds.upper, color = color), fatten = 2, width = 0.8)
} else {
ep <- ep + geom_crossbar(aes(ymin = lower, ymax = upper, color = color), fatten = 2, width = 0.8)
}
} else {
if(value == "log.odds.ratio") {
ep <- ep + geom_pointrange(aes(ymin = log.odds.lower, ymax = log.odds.upper, color = color), fatten = 0.5)
} else {
ep <- ep + geom_pointrange(aes(ymin = lower, ymax = upper, color = color), fatten = 0.5)
}
}
ep <- ep + scale_x_discrete(name = "Sample")
if(value == "log.odds.ratio") {
my.max <- round(max(variant.enrichment$odds.upper), 2) + 0.01
my.min <- round(min(variant.enrichment$odds.lower), 2) - 0.01
ep <- ep + scale_y_continuous(name = value.name)
ep <- ep + geom_hline(yintercept = 0, color = "#c4c4c4", alpha = 0.5) +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=log1p(1), alpha=0.1, fill="black")
} else {
my.max <- round(max(variant.enrichment$upper), 2) + 0.01
my.min <- round(min(variant.enrichment$lower), 2) - 0.01
ep <- ep + scale_y_continuous(name = value.name, breaks = round(seq(from = my.min, to = my.max, length.out = 5), 2))
ep <- ep + geom_hline(yintercept = 0, color = "#c4c4c4", alpha = 0.5) +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=0, alpha=0.1, fill="black")
}
if (all(!is.null(block1), !is.null(block2))) {
ep <- ep + theme(axis.text.x = element_blank(),
legend.position="bottom",
panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(),
strip.text.y = element_text(angle = 0, hjust = 0, vjust = 0.5, size = rel(1.5)),
strip.text.x = element_text(angle = 0, hjust = 0.5, vjust = 1, size = rel(1.5)))
} else {
ep <- ep + theme(axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5, size = rel(1.5)),
legend.position="bottom",
panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(),
strip.text.x = element_text(angle = 0, hjust = 0.5, vjust = 1, size = rel(1.5)))
}
if(!is.null(block1) & !is.null(block2)) {
my.form <- as.formula(paste(block1, "~", block2))
ep <- ep + facet_grid(my.form, scales = "free_x", space = "free_x", switch = "x")
} else if (!is.null(block1) & is.null(block2)) {
my.form <- as.formula(paste("~", block1))
ep <- ep + facet_wrap(my.form, ncol = ncol, strip.position = ifelse(ncol > 1L, "top", "right"))
} else if (is.null(block1) & !is.null(block2)) {
my.form <- as.formula(paste("~", block2))
ep <- ep + facet_wrap(my.form, ncol = ncol, strip.position = ifelse(ncol > 1L, "top", "right"))
}
if(color.is == "cont") {
ep <- ep + scale_color_brewer(palette = "Greens")
} else {
ep <- ep + scale_color_identity()
}
plot(ep)
return(invisible(ep))
}
GetVariantsInWindowVCF <- function(file, param, genome) {
vcf <- readVcf(file = file, genome = genome, param = param)
if (nrow(vcf) < 1L) stop("no variants found in interval; \n this is sometimes an error in fetching remote file, try with a local vcf")
vcf <- keepSeqlevels(vcf, seqlevelsInUse(vcf))
seqlevelsStyle(vcf) <- "UCSC"
metadata(vcf)$overlap.offset <- NA
return(vcf)
}
getFILE <- function(FILE, FUN, ..., N.TRIES=1L) {
N.TRIES <- as.integer(N.TRIES)
stopifnot(length(N.TRIES) == 1L, !is.na(N.TRIES))
while (N.TRIES > 0L) {
result <- tryCatch(FUN(FILE, ...), error = identity)
if (!inherits(result, "error"))
break
N.TRIES <- N.TRIES - 1L
}
if (N.TRIES == 0L) {
stop("'getFILE()' failed:",
"\nFILE: ", FILE,
"\nerror: ", conditionMessage(result))
}
result
}
make.overlap.matrix <- function(overlaps) {
overlap.table <- table(overlaps)
output.matrix <- matrix(overlap.table,
nrow = nrow(overlap.table),
dimnames = list(rownames(overlap.table),
colnames(overlap.table)))
if (!is(output.matrix, "matrix")) {
dim(output.matrix) <- c(length(output.matrix), 1)
dimnames(output.matrix) <- list(rownames(overlap.table),
colnames(overlap.table))
}
output.matrix[output.matrix > 1L] <- 1L
return(output.matrix)
}
enrich.segments <- function(fg, bg, features, CI, prior, strict.subset, return.overlaps) {
if (is(features, "GRangesList")) {
features <- unlist(features, use.names = FALSE)
}
if (all(c("sample", "state") %in% colnames(mcols(features)))) {
states <- unique(mcols(features)$state)
if (length(states) > 1L) {
myapply <- pblapply
} else {
myapply <- lapply
}
enrichment <- myapply(states, function(state,
local.features = features,
local.fg = fg,
local.bg = bg,
local.CI = CI,
local.prior = prior,
local.strict.subset = strict.subset,
local.return.overlaps = return.overlaps) {
local.features <- local.features[mcols(local.features)$state %in% state, ]
local.fg <- SetOverlaps(local.fg, local.features)
local.bg <- SetOverlaps(local.bg, local.features)
## Equalize feature columns
fg.features <- colnames(mcols(ShowOverlaps(local.fg)))
bg.features <- colnames(mcols(ShowOverlaps(local.bg)))
all.features <- union(fg.features, bg.features)
## first fg
if (is(local.fg, "VCF")) {
if(any(!(is.element(all.features, fg.features)))) {
mcols(rowRanges(local.fg))[, all.features[!(is.element(all.features, fg.features))]] <- 0L
}
mcols(rowRanges(local.fg)) <- cbind(mcols(rowRanges(local.fg))[, 1:metadata(local.fg)$overlap.offset - 1],
mcols(rowRanges(local.fg))[, all.features, drop = FALSE])
} else if (is(local.fg, "GRanges")) {
if (any(!(is.element(all.features, fg.features)))) {
mcols(local.fg)[, all.features[!(is.element(all.features, fg.features))]] <- 0L
}
mcols(local.fg) <- cbind(mcols(local.fg)[, 1:attributes(local.fg)$metadata$overlap.offset - 1],
mcols(local.fg)[, all.features, drop = FALSE])
}
## then bg
if (is(local.bg, "VCF")) {
if(any(!(is.element(all.features, bg.features)))) {
mcols(rowRanges(local.bg))[, all.features[!(is.element(all.features, bg.features))]] <- 0L
}
mcols(rowRanges(local.bg)) <- cbind(mcols(rowRanges(local.bg))[, 1:metadata(local.bg)$overlap.offset - 1],
mcols(rowRanges(local.bg))[, all.features, drop = FALSE])
} else if (is(local.bg, "GRanges")) {
if (any(!(is.element(all.features, bg.features)))) {
mcols(local.bg)[, all.features[!(is.element(all.features, bg.features))]] <- 0L
}
mcols(local.bg) <- cbind(mcols(local.bg)[, 1:attributes(local.bg)$metadata$overlap.offset - 1],
mcols(local.bg)[, all.features, drop = FALSE])
}
enrich <- enrich.features(fg = local.fg,
bg = local.bg,
CI = local.CI,
prior = local.prior,
strict.subset = local.strict.subset)
if (local.return.overlaps) {
return(list(e = enrich, fg.o = ShowOverlaps(local.fg), bg.o = ShowOverlaps(local.bg)))
} else {
return(enrich)
}
})
if(return.overlaps) {
overlaps <- lapply(enrichment, function(x) {
overlaps <- GRangesList(foreground.overlaps = x$fg.o, background.overlaps = x$bg.o)
return(overlaps)
})
names(overlaps) <- states
enrichment <- lapply(enrichment, function(x) {
return(x$e)
})
}
names(enrichment) <- states
for (state in states) {
enrichment[[state]]$state <- state
}
enrichment <- do.call("rbind", enrichment)
} else {
stop("Segmentations must have mcols with fields 'sample' and 'state'")
}
if(return.overlaps) {
return(list(overlaps = overlaps, enrichment = enrichment))
} else {
return(enrichment)
}
}
enrich.features <- function(fg, bg, CI, prior, strict.subset) {
## foreground overlaps
fg.over.matrix <- as.matrix(mcols(ShowOverlaps(fg)))
rownames(fg.over.matrix) <- rownames(fg)
## background overlaps
bg.over.matrix <- as.matrix(mcols(ShowOverlaps(bg)))
rownames(bg.over.matrix) <- rownames(bg)
## start enrichment
sample.size <- dim(fg.over.matrix)[[1]]
sample.stats <- colSums(fg.over.matrix)
if (strict.subset) {
total.size <- dim(bg.over.matrix)[[1]]
total.stats <- colSums(bg.over.matrix)
} else {
total.size <- dim(bg.over.matrix)[[1]] + sample.size
total.stats <- colSums(bg.over.matrix) + sample.stats
}
if (exists("prior") & !is.null(prior)) {
a <- prior[["a"]]
b <- prior[["b"]]
} else {
stop("no argument present for prior, needed for simulation")
}
enrichment <- data.frame(sample = character(),
fg.ratio = numeric(),
bg.ratio = numeric(),
probability = numeric(),
difference = numeric(),
lower = numeric(),
upper = numeric(),
fg.success = numeric(),
fg.total = numeric(),
bg.success = numeric(),
bg.total = numeric(),
stringsAsFactors = FALSE)
CI <- (1 - CI)/2
for (my.sample in seq_along(colnames(bg.over.matrix))) {
total.success <- total.stats[my.sample]
sample.success <- sample.stats[my.sample]
n1 <- sample.size
y1 <- sample.success
n2 <- total.size - sample.size
y2 <- total.success - sample.success
# SIMULATION
I = 100000 # simulations
theta1 = rbeta(I, y1 + a, (n1 - y1) + b)
theta2 = rbeta(I, y2 + a, (n2 - y2) + b)
diff = theta1 - theta2
# OUTPUT
quantiles <- try(quantile(diff,c(CI, 0.5, 1 - CI)))
#if(inherits(quantiles, "try-error")) browser()
fisher.res <- fisher.test(matrix(c(sample.success,
total.success - sample.success,
sample.size - sample.success,
(total.size - total.success) - sample.size + sample.success),
2, 2),
conf.level = CI)
result <- data.frame(sample = colnames(bg.over.matrix)[my.sample],
fg.ratio = sample.success/sample.size,
bg.ratio = total.success/total.size,
probability = mean(theta1 > theta2),
log.odds.ratio = log1p(fisher.res$estimate),
log.odds.lower = log1p(fisher.res$conf.int[[1]]),
log.odds.upper = log1p(fisher.res$conf.int[[2]]),
difference = quantiles[2],
lower = quantiles[1],
upper = quantiles[3],
fg.success = sample.success,
fg.total = sample.size,
bg.success = total.success,
bg.total = total.size,
stringsAsFactors = FALSE)
enrichment <- rbind(enrichment, result)
}
enrichment$significant <- FALSE
enrichment[enrichment$probability > (1 - CI) | enrichment$probability < CI, "significant"] <- TRUE
return(enrichment)
}
clump_data <- function(dat, clump_kb=10000, clump_r2=0.001, clump_p1=1, clump_p2=1)
{
.Deprecated("ieugwasr::ld_clump()")
if(missing(clump_r2))
{
}
if(!is.data.frame(dat))
{
stop("Expecting data frame returned from format_data")
}
if(! "pval.exposure" %in% names(dat))
{
dat$pval.exposure <- 0.99
}
if(! "id.exposure" %in% names(dat))
{
dat$id.exposure <- random_string(1)
}
d <- data.frame(rsid=dat$SNP, pval=dat$pval.exposure, id=dat$id.exposure)
out <- ieugwasr::ld_clump(d, clump_kb=clump_kb, clump_r2=clump_r2, clump_p=clump_p1)
keep <- paste(dat$SNP, dat$id.exposure) %in% paste(out$rsid, out$id)
return(dat[keep, ])
}
ld_matrix <- function(snps, with_alleles=TRUE)
{
.Deprecated("ieugwasr::ld_matrix()")
ieugwasr::ld_matrix(variants=snps, with_alleles=with_alleles)
}
# setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
zScore <- function (cntA, totA, cntB, totB) {
#calculate
avgProportion <- (cntA + cntB) / (totA + totB)
probA <- cntA/totA
probB <- cntB/totB
SE <- sqrt(avgProportion * (1-avgProportion)*(1/totA + 1/totB))
zScore <- (probA-probB) / SE
return (zScore)
}
######################
### main functions ###
######################
#--------------------------- read inputs
#------- chromatin_states
chromatin_states <- GRanges(sample = BackendData_ChromatinStates[[4]],
seqnames = gsub('chr','',BackendData_ChromatinStates[[1]]),
ranges = IRanges(BackendData_ChromatinStates[[2]], BackendData_ChromatinStates[[3]]),
state = BackendData_ChromatinStates[[4]])
#------- BackendData_GenomicFeatures
BackendData_GenomicFeatures$seqnames <- gsub('chr','',BackendData_GenomicFeatures$seqnames)
BackendData_GenomicFeatures <- BackendData_GenomicFeatures[which(BackendData_GenomicFeatures$seqnames %in% c(1:22, 'X')),]
BackendData_GenomicFeatures <- GRanges(sample = BackendData_GenomicFeatures$sample,
seqnames = BackendData_GenomicFeatures$seqnames,
ranges = IRanges(BackendData_GenomicFeatures$start, BackendData_GenomicFeatures$end),
state = BackendData_GenomicFeatures$state)
#--------------------------------- input
#- query
indexSNP <- SNP
SNP_chr_POS <- snps.from.rsid(rsid = SNP, dbSNP = SNPlocs.Hsapiens.dbSNP144.GRCh38, search.genome = BSgenome.Hsapiens.UCSC.hg38)
SNP_chr_POS <- data.frame(SNP_chr_POS, stringsAsFactors = FALSE)
posQuerySNP <- GRanges(paste0(gsub('chr','',as.character(SNP_chr_POS$seqnames)),':',as.character(SNP_chr_POS$start),'-',as.character(SNP_chr_POS$end))) + windowSize
#Using the 1MB window that we defined for the index variant, we capture the relevant variants from the VCF file for our LD calculations.
if(!is.null(vcfPATH))
{
#---- vcf is the address of the vcf file
chr.remote.vcf <- vcfPATH
vcfsubset <- NULL
vcfsubset <- GetVariantsInWindow(file = chr.remote.vcf,position = posQuerySNP[1], type = "vcf")
my.samples <- fread(vcfMetaData, stringsAsFactors = FALSE)
vcfsubset <- SetPopulation(vcfsubset, sample_sheet = my.samples)
row.names(vcfsubset) <- make.names(row.names(vcfsubset), unique = TRUE)
}
LD <- CalcLD(vcfsubset, index = indexSNP, population = "EUR")
#----------------- foreground
vcfsubsetsnps <- SplitVcfLd(vcf = LD, ld = c(metric = "R.squared", cutoff = 1, maf = mafThreshold), strict.subset = TRUE) #a strict subset cannot be the same set, that is, it cannot contain all of the elements that the other set does. Or in other words, a strict subset must be smaller, while a subset can be the same size.
length( as.character(names(vcfsubsetsnps$fg)) )
length( as.character(names(vcfsubsetsnps$bg)) )
F1 <- as.character(names(vcfsubsetsnps$fg))
fg_variants <- snps.from.rsid(rsid = F1, dbSNP = SNPlocs.Hsapiens.dbSNP144.GRCh38, search.genome = BSgenome.Hsapiens.UCSC.hg38)
LD <- CalcLD(vcfsubset, index = indexSNP, population = "EUR")
vcfsubsetsnps <- SplitVcfLd(vcf = LD, ld = c(metric = "R.squared", cutoff = 0.001, maf = mafThreshold), strict.subset = TRUE) #a strict subset cannot be the same set, that is, it cannot contain all of the elements that the other set does. Or in other words, a strict subset must be smaller, while a subset can be the same size.
length( as.character(names(vcfsubsetsnps$fg)) )
length( as.character(names(vcfsubsetsnps$bg)) )
bg_variants <- snps.from.rsid(rsid = as.character(names(vcfsubsetsnps$bg)),
dbSNP = SNPlocs.Hsapiens.dbSNP144.GRCh38,
search.genome = BSgenome.Hsapiens.UCSC.hg38)
length(names(vcfsubsetsnps$bg))
foreground_sub <- as.data.frame(fg_variants)
foreground_sub$seqnames <- gsub('chr', '', foreground_sub$seqnames)
fGRhg38 <- GRanges(
seqnames <- Rle(as.character(foreground_sub$seqnames)),
ranges <- IRanges(start=as.numeric(foreground_sub$start), end=as.numeric(foreground_sub$end)+1, names=foreground_sub$SNP_ID),
strand <- Rle(as.character(foreground_sub$strand))
)
background_sub <- as.data.frame(bg_variants)
background_sub$seqnames <- gsub('chr', '', background_sub$seqnames)
bGRhg38 <- GRanges(
seqnames<-Rle(as.character(background_sub$seqnames)),
ranges <- IRanges(start=as.numeric(background_sub$start), end=as.numeric(background_sub$end)+1, names=background_sub$SNP_ID),
strand <- Rle(as.character(background_sub$strand))
)
#--------------------------------- CS enrichment
#--- enrich foreground
fo <- findOverlaps(query=fGRhg38, subject=chromatin_states, type="any")
foreground_CS <- chromatin_states[subjectHits(fo),]
#--- enrich background
fo <- findOverlaps(query=bGRhg38, subject=chromatin_states, type="any")
Background_CS <- chromatin_states[subjectHits(fo),]
CSs <- unique(foreground_CS$sample)
for(t in 1:length(CSs))
{
cntA<-length(which(foreground_CS$sample==CSs[t]))
totA<-length(fGRhg38)
cntB<-length(which(Background_CS$sample==CSs[t]))
totB<-length(bGRhg38)
table <- c(cntA, totA, cntB, totB)
dim(table)<-c(2,2)
fishert <- fisher.test(table)
z_score <- zScore(cntA, totA, cntB, totB)
temp <- data.frame(SNP = indexSNP, CS = CSs[t], p = fishert$p.value, oddsratio = fishert$estimate, CIl= fishert$conf.int[1], CIh = fishert$conf.int[2], zScore = z_score, FE = cntA, FA = totA, BE = cntB, BA = totB)
if(t==1){RESULTsChromatinState = temp}else{RESULTsChromatinState = rbind(RESULTsChromatinState, temp)}
print(z_score)
}
#------------------- genomic features
#--- enrich foreground
fo <- findOverlaps(query=fGRhg38, subject=BackendData_GenomicFeatures, type="any")
foreground_GR <- BackendData_GenomicFeatures[subjectHits(fo),]
#--- enrich background
fo <- findOverlaps(query=bGRhg38, subject=BackendData_GenomicFeatures, type="any")
Background_GR <- BackendData_GenomicFeatures[subjectHits(fo),]
if(length(foreground_GR)>0)
{
GRs <- unique(foreground_GR$sample)
for(t in 1:length(GRs))
{
cntA<-length(which(foreground_GR$sample==GRs[t]))
totA<-length(foreground_GR)
cntB<-length(which(Background_GR$sample==GRs[t]))
totB<-length(Background_GR)
table <- c(cntA, totA, cntB, totB)
dim(table)<-c(2,2)
fishert <- fisher.test(table)
z_score <- zScore(cntA, totA, cntB, totB)
temp <- data.frame(SNP = indexSNP, GR = GRs[t], p = fishert$p.value, oddsratio = fishert$estimate, CIl= fishert$conf.int[1], CIh = fishert$conf.int[2], zScore = z_score, FE = cntA, FA = totA, BE = cntB, BA = totB)
if(t==1){RESULTsGenomicFeatures = temp}else{RESULTsGenomicFeatures = rbind(RESULTsGenomicFeatures, temp)}
print(z_score)
}
}
#------------------- save outputs
Destiny_Folder <- system.file(package = "FEVV")
Destiny_Folder <- paste(Destiny_Folder, "/RESULTsGenomicFeatures.txt", sep = "")
write.table(
RESULTsGenomicFeatures, Destiny_Folder, sep = "\t", row.names = FALSE, quote = FALSE
)
Destiny_Folder <- system.file(package = "FEVV")
Destiny_Folder <- paste(Destiny_Folder, "/RESULTsChromatinState.txt", sep = "")
write.table(
RESULTsChromatinState, Destiny_Folder, sep = "\t", row.names = FALSE, quote = FALSE
)
print("You can find the results at: ")
print(system.file(package="FEVV"))
}
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