R/xSNP2eGenes.r

In Pi: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level

#' Function to define eQTL genes given a list of SNPs or a customised eQTL mapping data
#'
#' \code{xSNP2eGenes} is supposed to define eQTL genes given a list of SNPs or a customised eQTL mapping data. The eQTL weight is calcualted as Cumulative Distribution Function of negative log-transformed eQTL-reported signficance level. 
#'
#' @param data an input vector containing SNPs. SNPs should be provided as dbSNP ID (ie starting with rs). Alternatively, they can be in the format of 'chrN:xxx', where N is either 1-22 or X, xxx is number; for example, 'chr16:28525386'
#' @param include.eQTL the eQTL supported currently. By default, it is 'NA' to disable this option. Pre-built eQTL datasets are detailed in \code{\link{xDefineEQTL}}
#' @param eQTL.customised a user-input matrix or data frame with 4 columns: 1st column for SNPs/eQTLs, 2nd column for Genes, 3rd for eQTL mapping significance level (p-values or FDR), and 4th for contexts (required even though only one context is input). Alternatively, it can be a file containing these 4 columns. It is designed to allow the user analysing their eQTL data. This customisation (if provided) will populate built-in eQTL data
#' @param cdf.function a character specifying a Cumulative Distribution Function (cdf). It can be one of 'exponential' based on exponential cdf, 'empirical' for empirical cdf
#' @param plot logical to indicate whether the histogram plot (plus density or CDF plot) should be drawn. By default, it sets to false for no plotting
#' @param verbose logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display
#' @param RData.location the characters to tell the location of built-in RData files. See \code{\link{xRDataLoader}} for details
#' @param guid a valid (5-character) Global Unique IDentifier for an OSF project. See \code{\link{xRDataLoader}} for details
#' @return
#' a data frame with following columns:
#' \itemize{
#'  \item{\code{Gene}: eQTL-containing genes}
#'  \item{\code{SNP}: eQTLs}
#'  \item{\code{Sig}: the eQTL mapping significant level (the best/minimum)}
#'  \item{\code{Weight}: the eQTL weight}
#' }
#' @note none
#' @export
#' @seealso \code{\link{xDefineEQTL}}, \code{\link{xSymbol2GeneID}}
#' @include xSNP2eGenes.r
#' @examples
#' RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
#' \dontrun{
#' # a) provide the SNPs with the significance info
#' data(ImmunoBase)
#' gr <- ImmunoBase$AS$variants
#' AS <- as.data.frame(GenomicRanges::mcols(gr)[, c('Variant','Pvalue')])
#'
#' # b) define eQTL genes
#' df_eGenes <- xSNP2eGenes(data=AS[,1], include.eQTL="JKscience_TS2A", RData.location=RData.location)
#' }

xSNP2eGenes <- function(data, include.eQTL=NA, eQTL.customised=NULL, cdf.function=c("empirical","exponential"), plot=FALSE, verbose=TRUE, RData.location="http://galahad.well.ox.ac.uk/bigdata", guid=NULL)
{

    ## match.arg matches arg against a table of candidate values as specified by choices, where NULL means to take the first one
    cdf.function <- match.arg(cdf.function)

	## replace '_' with ':'
	data <- gsub("_", ":", data, perl=TRUE)
	## replace 'imm:' with 'chr'
	data <- gsub("imm:", "chr", data, perl=TRUE)
    
    data <- unique(data)
    
	if(verbose){
		now <- Sys.time()
		message(sprintf("A total of %d SNPs are input", length(data)), appendLF=TRUE)
	}
    
    ######################################################
    # Link to targets based on eQTL
    ######################################################
    df_SGS <- xDefineEQTL(data=NULL, include.eQTL=include.eQTL, eQTL.customised=eQTL.customised, verbose=verbose, RData.location=RData.location, guid=guid)
	
	if(!is.null(df_SGS)){	
		
		uid <- paste(df_SGS[,1], df_SGS[,2], sep='_')
		df <- cbind(uid, df_SGS)
		res_list <- split(x=df$Sig, f=df$uid)
		vec <- unlist(lapply(res_list, min))
		raw_score <- -1*log10(vec)
		
		if(cdf.function == "exponential"){
			##  fit raw_score to the cumulative distribution function (CDF; depending on exponential empirical distributions)
			lambda <- MASS::fitdistr(raw_score, "exponential")$estimate
		
			## eQTL weight for input SNPs
			ind <- match(df_SGS[,1], data)
			df <- data.frame(df_SGS[!is.na(ind),])
			## weights according to eQTL
			wE <- stats::pexp(-log10(df$Sig), rate=lambda)
			
			#########
			if(nrow(df)==0){
				df_eGenes <- NULL
			}else{
				df_eGenes <- data.frame(Gene=df$Gene, SNP=df$SNP, Sig=df$Sig, Weight=wE, row.names=NULL, stringsAsFactors=FALSE)
			}
			#########
			
			if(plot){
				hist(raw_score, breaks=1000, freq=FALSE, col="grey", xlab="-log10(p-values)", main="")
				curve(stats::dexp(x=raw_score,rate=lambda), 0:max(raw_score), col=2, add=TRUE)
			}
			
			if(verbose){
				now <- Sys.time()
				message(sprintf("eQTL weights are CDF of exponential empirical distributions (parameter lambda=%f)", lambda), appendLF=TRUE)
			}
			
		}else if(cdf.function == "empirical"){
			## Compute an empirical cumulative distribution function
			my.CDF <- stats::ecdf(raw_score)
			
			## eQTL weight for input SNPs
			ind <- match(df_SGS[,1], data)
			df <- data.frame(df_SGS[!is.na(ind),])
			## weights according to eQTL
			wE <- my.CDF(-log10(df$Sig))
			
			#########
			if(nrow(df)==0){
				df_eGenes <- NULL
			}else{
				df_eGenes <- data.frame(Gene=df$Gene, SNP=df$SNP, Sig=df$Sig, Weight=wE, row.names=NULL, stringsAsFactors=FALSE)
				df_eGenes <- df_eGenes[order(df_eGenes$Gene,df_eGenes$Sig,df_eGenes$SNP,decreasing=FALSE),]
			}
			#########
			
			if(plot){
				plot(my.CDF, xlab="-log10(p-values)", ylab="Empirical CDF (eQTL weights)", main="")
			}
			
			if(verbose){
				now <- Sys.time()
				message(sprintf("eQTL weights are CDF of empirical distributions"), appendLF=TRUE)
			}
			
		}
	
		if(verbose){
			now <- Sys.time()
			message(sprintf("%d eGenes are defined involving %d eQTL", length(unique(df_eGenes$Gene)), length(unique(df_eGenes$SNP))), appendLF=TRUE)
		}
	
	}else{
		df_eGenes <- NULL
		
		if(verbose){
			now <- Sys.time()
			message(sprintf("No eQTL genes are defined"), appendLF=TRUE)
		}
	}
	
	####################################
	# only keep those genes with GeneID
	####################################
	if(!is.null(df_eGenes)){
		ind <- xSymbol2GeneID(df_eGenes$Gene, details=FALSE, verbose=verbose, RData.location=RData.location, guid=guid)
		df_eGenes <- df_eGenes[!is.na(ind), ]
		if(nrow(df_eGenes)==0){
			df_eGenes <- NULL
		}
	}
	####################################
	
    invisible(df_eGenes)
}