#' Computation of HBD probabilities and FLOD scores by snps
#'
#' This function is used to compute HBD probabilities and FLOD scores on individuals in a sample
#'
#' @param atlas an atlas object
#' @param w.id The indices of individals for which HBD probabilities are computed
#' @details This function iterates over the slots submaps_list of the atlas object.
#' @details For each submaps in the slots submaps_list of the object, the slots HBD_recap and FLOD_recap will be updated with a matrix of dimension : number_individual x number_of_markers
#'
#' @return the atlas object with HBD_recap and FLOD_recap by snps
#'
#' @seealso \code{\link{setHFLOD}}
#'
#' @export
setHBDProbAndFLODBySnps <- function(atlas, w.id, q = 1e-4)
{
if(class(atlas@submaps_list[[1]])[1] != "snpsMatrix" & class(atlas@submaps_list[[1]])[1] != "HostspotsMatrix")
stop("need either an hotspots.segments list of submaps or a snpsSegments list of submaps.")
if(class(atlas@bedmatrix)[1] != "bed.matrix")
stop("Need a bed.matrix.")
id <- as.vector(atlas@submap_summary$id[w.id])
famid <- as.vector(atlas@submap_summary$famid[w.id])
h <- new.env()
for(i in seq_along(atlas@submaps_list)) {
HBD_prob <- matrix(0.0, nrow = length(w.id), ncol = atlas@submaps_list[[i]]@ncol)#HBD matrix
dimnames(HBD_prob) <- list( uniqueIds(famid,id), atlas@submaps_list[[i]]@map$id)
FLOD <- matrix(0.0, nrow = length(w.id), ncol = atlas@submaps_list[[i]]@ncol)#HBD matrix
dimnames(FLOD) <- list( uniqueIds(famid,id), atlas@submaps_list[[i]]@map$id)
for (j in seq_len(nrow(HBD_prob))) {
j1 <- w.id[j]
if(!is.na(atlas@submaps_list[[i]]@a[j1]) & (atlas@submaps_list[[i]]@a[j1] <= 1) & !is.na(atlas@submaps_list[[i]]@f[j1])) {
HBD_prob[j,seq_len(ncol(HBD_prob))] <-forwardBackward(getLogEmiss(atlas@submaps_list[[i]], j1),
atlas@submaps_list[[i]]@delta.dist,
atlas@submaps_list[[i]]@a[j1],
atlas@submaps_list[[i]]@f[j1] )[2,]
}
HBD_prob[!is.finite(HBD_prob)] <- 0
if( (atlas@submaps_list[[i]]@a[j1] < 1) & !is.na(atlas@submaps_list[[i]]@f[j1]) ) {
FLOD[j,] <- log10((HBD_prob[j,] + q * ( 1 - HBD_prob[j,]))/
(atlas@submaps_list[[i]]@f[j1] + q * ( 1 - atlas@submaps_list[[i]]@f[j1])))
}
}
for (k in seq_along(atlas@submaps_list[[i]]@map$id)) {
if (length(h[[atlas@submaps_list[[i]]@map$id[[k]]]][[1]]) == 0) {
h[[atlas@submaps_list[[i]]@map$id[[k]]]][['HBD_prob']] <- HBD_prob[,k]
h[[atlas@submaps_list[[i]]@map$id[[k]]]][['FLOD']] <- FLOD[,k]
h[[atlas@submaps_list[[i]]@map$id[[k]]]][['counts']] <- 1
} else {
h[[atlas@submaps_list[[i]]@map$id[[k]]]][['HBD_prob']] <- h[[atlas@submaps_list[[i]]@map$id[[k]]]][['HBD_prob']] + HBD_prob[,k]
h[[atlas@submaps_list[[i]]@map$id[[k]]]][['FLOD']] <- h[[atlas@submaps_list[[i]]@map$id[[k]]]][['FLOD']] + FLOD[,k]
h[[atlas@submaps_list[[i]]@map$id[[k]]]][['counts']] <- h[[atlas@submaps_list[[i]]@map$id[[k]]]][['counts']] + 1
}
}
}
for (n in names(h)) {
h[[n]][['HBD_prob']] <- h[[n]][['HBD_prob']] / h[[n]][['counts']]
h[[n]][['FLOD']] <- h[[n]][['FLOD']] / h[[n]][['counts']]
}
hbd <- NULL
hbd <- as.data.frame(lapply(names(h), function (i) cbind(hbd, h[[i]][['HBD_prob']])))
colnames(hbd) <- names(h)
hbd <- as.matrix(hbd)
atlas@HBD_recap <- hbd
flod <- NULL
flod <- as.data.frame(lapply(names(h), function (i) cbind(flod, h[[i]][['FLOD']])))
colnames(flod) <- names(h)
flod <- as.matrix(flod)
atlas@FLOD_recap <- flod
po <- match(colnames(atlas@HBD_recap), atlas@bedmatrix@snps$id)
snp.chr <- atlas@bedmatrix@snps$chr[po]
snp.pos <- atlas@bedmatrix@snps$pos[po]
atlas@HBD_recap <- atlas@HBD_recap[, order(snp.chr, snp.pos)]
atlas@FLOD_recap <- atlas@FLOD_recap[, order(snp.chr, snp.pos)]
atlas
}
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