R/binom.test-methods.R

In AllelicImbalance: Investigates Allele Specific Expression

#'@include chisq.test-methods.R
NULL

#' binomial test
#' 
#' Performs a binomial test on an ASEset object.
#' 
#' the test can only be applied to one strand at the time.
#' 
#' @name binom.test
#' @rdname binom.test
#' @aliases binom.test,ASEset-method
#' @docType methods
#' @param x \code{ASEset} object
#' @param n strand option
#' @return \code{binom.test} returns a matrix
#' @author Jesper R. Gadin, Lasse Folkersen
#' @seealso \itemize{ \item The \code{\link{chisq.test}} which is another test
#' that can be applied on an \link{ASEset} object.  }
#' @keywords binomial test
#' @examples
#' 
#' #load example data
#' data(ASEset)
#' 
#' #make a binomial test
#' binom.test(ASEset,'*')
#' 
#' 
NULL

#' @rdname binom.test
#' @export 
setMethod("binom.test", signature(x = "ASEset", n = "ANY"), function(x, n = "*") {
    
	if(!("genotype" %in% assayNames(x))){
		genotype(x) <- inferGenotypes(x)
	}

    strand <- n

	fr <- fraction(x, strand=strand,
			top.fraction.criteria="ref",
			threshold.count.sample= 1)
    
    # checks
    if (!sum(strand %in% c("+", "-", "*")) > 0) {
        stop("strand parameter n has to be either '+', '-' or '*' ")
    }
	idx <- which(!is.na(fr))
	arn <- arank(x,return.type="names",return.class="matrix") 
	ac <- alleleCounts(x, strand=strand, return.class="array")

	mat1 <- matrix(aperm(ac,c(3,2,1))[aperm(array(matrix(x@variants, ncol=length(x@variants),
		 nrow=nrow(x), byrow=TRUE)==arn[,1]
	 ,dim=c(nrow(x), length(x@variants), ncol=ncol(x))),c(2,3,1))
	],ncol=ncol(x),nrow=nrow(x), byrow=TRUE, dimnames=list(rownames(x),colnames(x)))

	mat2 <- matrix(aperm(ac,c(3,2,1))[aperm(array(matrix(x@variants, ncol=length(x@variants),
		 nrow=nrow(x), byrow=TRUE)==arn[,2]
	 ,dim=c(nrow(x), length(x@variants), ncol=ncol(x))),c(2,3,1))
	],ncol=ncol(x),nrow=nrow(x), byrow=TRUE, dimnames=list(rownames(x),colnames(x)))


	allele1 <- mat1[idx] 
	allele2 <- mat2[idx]

	#test the two most expressed alleles
	biasmat1 <- matrix(aperm(mapBias(x, return.class="array"),c(3,2,1))[aperm(array(matrix(
		x@variants, ncol=length(x@variants),
		 nrow=nrow(x), byrow=TRUE)==arn[,1]
	 ,dim=c(nrow(x), length(x@variants), ncol=ncol(x))),c(2,3,1))
	],ncol=ncol(x),nrow=nrow(x), byrow=TRUE, dimnames=list(rownames(x),colnames(x)))


	biasAllele1 <- biasmat1[idx] 
	
	#maybe put a check here later that bias1 and bias2 sum to 1

	ml <- mapply(allele1,allele2,biasAllele1,FUN=function(x,y,z){
		binom.test(c(x,y), p = z)[[3]]
	})

	#cerate matrix in same size as fr	
	pv <- fr	
	pv[idx] <- ml

	t(pv)
    
}) 

##' @rdname binom.test
##'  @export 
#setMethod("binom.test", signature(x = "ASEset", n = "ANY"), function(x, n = "*") {
#    
#    strand <- n
#    
#    # checks
#    if (!sum(strand %in% c("+", "-", "*")) > 0) {
#        stop("strand parameter n has to be either '+', '-' or '*' ")
#    }
#    
#    
#    biasWarning <- vector()
#    pLst <- list()
#    
#    # use this strand to get dimension information
#    if (strand == "both") {
#        tmpStrand <- "+"
#    } else {
#        tmpStrand <- strand
#    }
#    
#    for (i in 1:length(alleleCounts(x, strand = tmpStrand))) {
#        bias <- mapBias(x)[[i, drop = FALSE]]
#        
#        df <- alleleCounts(x, tmpStrand)[[i]]
#        returnVec <- rep(NA, nrow(df))
#        for (j in 1:nrow(df)) {
#            so <- sort(df[j, ], decreasing = TRUE)
#            if (so[2] != 0) {
#                # place bias in same order as so
#                bi <- bias[, names(so)[1:2]]
#                
#                # check that bi[1] and bi[2] adds up to 100% or force 0.5-0.5 and give warning
#                if (bi[1] + bi[2] != 1) {
#                  bi[1] <- bi[2] <- 0.5
#                  warning("Found disrepancy between mapping bias information and mapBiasExpMean and the allele count. Coerced to expectation of 0.5 to 0.5 ratio")
#                }
#                expCounts <- c(sum(so) * bi[1], sum(so) * bi[2])
#                returnVec[j] <- binom.test(as.numeric(c(so[1], so[2])), p = as.numeric(bi[1]/(bi[1] + 
#                  bi[2])))[[3]]
#            } else {
#                returnVec[j] <- NA
#            }  # if it's mono-allelic
#        }  #end for-loop
#        pLst[[names(alleleCounts(x, strand = tmpStrand)[i])]] <- returnVec
#    }
#    if (length(biasWarning) > 0) {
#        warning(paste(length(biasWarning), "SNPs had disrepancy between their two highest read count alleles and the\n\n\t\t\t\t\t\t\t\t\ttwo alleles indicated in mapBiasExpMean. They were coerced to a standard and expected alignment \n\t\t\t\t\t\t\t\t\tbias of 0.5 to 0.5, but could be checked further as this indicates non-standard allele distributions:\n", 
#            paste(biasWarning[1:(min(c(length(biasWarning), 20)))], collapse = ", ")))
#    }
#    return(as.matrix(as.data.frame(pLst)))
#})