R/aggregation.R
In ETHZ-INS/scanMiR: scanMiR
Defines functions
.aggregateSiteInfo
.aggregate_miRNA
aggregateMatches
Documented in
aggregateMatches
#' aggregateMatches
#'
#' Aggregates miRNA binding sites with log_kd values to predict transcript
#' repression. See the vignette for more detail.
#'
#' @param m A GRanges or data.frame of matches as returned by `findSeedMatches`.
#' @param a The relative concentration of unbound AGO-miRNA complexes.
#' @param b Factor specifying the additional repression by a single bound AGO.
#' @param c Penalty for sites that are found within the ORF region.
#' @param p3 Factor specifying additional repression due to 3p alignment.
#' @param coef_utr Factor specifying additional repression due to UTR length.
#' @param coef_orf Factor specifying additional repression due to ORF length.
#' @param p3.range Range used for 3p alignment.
#' @param keepSiteInfo Logical; whether to return information about site types
#' (default = TRUE). Ignored if `m` does not contain `log_kd` values
#' @param toInt Logical; whether to convert repression scores to integers
#' (default = FALSE).
#' @param BP Pass `BiocParallel::MulticoreParam(ncores, progressbar=TRUE)` to
#' enable multithreading. Note that in addition, `aggregateMatches` uses the
#' \link{data.table} package, which is often set to use multi-threading by
#' default (which would be multiplied by threads determined by `BP`). See
#' \code{\link[data.table]{setDTthreads}} for more information.
#'
#' @return a data.frame containing aggregated repression values and/or
#' information about the numbers and types of matches
#' @export
#' @importFrom stats quantile
#' @importFrom data.table as.data.table .N := dcast rbindlist .SD
#'
#' @examples
#' # we create mock RNA sequences and seeds:
#' seqs <- getRandomSeq(n=10)
#'
#' # load sample KdModel
#' data(SampleKdModel)
#'
#' # find matches
#' matches <- findSeedMatches(seqs, SampleKdModel)
#'
#' # aggregate matches
#' aggregateMatches(matches)
aggregateMatches <- function(m, a=0.007726 , b=0.5735, c=0.1810, p3=0.051,
coef_utr=0, coef_orf=0, p3.range=c(3L,8L),
keepSiteInfo = TRUE, toInt=FALSE, BP=NULL){
if(is.null(BP)) BP <- BiocParallel::SerialParam()
ll <- NULL # length info
if(is(m,"GRanges")){
if(length(m)==0) stop("Your search did not result in any match.")
if(!is.null(ll <- metadata(m)$tx_info)){
ll <- ll[,c("ORF.length", "UTR.length")]
colnames(ll) <- tolower(colnames(ll))
ll$transcript <- row.names(ll)
}
m$transcript <- as.factor(seqnames(m))
m <- mcols(m)
if(!is.null(m$miRNA)) m$miRNA <- droplevels(as.factor(m$miRNA))
m <- as.data.frame(m)
}else{
if(nrow(m)==0) stop("Your search did not result in any match.")
if(!is.null(ll <- attr(m, "tx_info"))){
ll <- ll[,c("ORF.length", "UTR.length")]
colnames(ll) <- tolower(colnames(ll))
ll$transcript <- row.names(ll)
}
}
if(!is.null(m$miRNA)){
m <- split(m, m$miRNA)
m <- bplapply(m, BPPARAM=BP, FUN=function(x){
.aggregate_miRNA(x, ll=ll, a=a, b=b, c=c, p3=p3,coef_utr = coef_utr,
coef_orf = coef_orf, keepSiteInfo = keepSiteInfo,
toInt=toInt, p3.range=p3.range)
})
m <- as.data.frame(data.table::rbindlist(m, use.names=TRUE, fill=TRUE,
idcol = "miRNA"))
m$miRNA <- as.factor(m$miRNA)
for(f in colnames(m)){
if(is.numeric(m[[f]])) m[[f]][is.na(m[[f]])] <- 0L
}
}else{
m <- .aggregate_miRNA(m, ll=ll, a=a, b=b, c=c, p3=p3,coef_utr = coef_utr,
coef_orf = coef_orf, keepSiteInfo = keepSiteInfo,
toInt=toInt, p3.range=p3.range)
}
return(m)
}
.aggregate_miRNA <- function(m, ll = NULL, a=0.007726, b=0.5735, c=0.1810,
p3=0.051, coef_utr = -0.17106,
coef_orf = -0.21546, p3.range=c(3L,8L),
keepSiteInfo = FALSE, toInt=FALSE){
if(is(m,"GRanges")){
m$transcript <- as.factor(seqnames(m))
m <- mcols(m)
if(!is.null(m$miRNA)) m$miRNA <- droplevels(as.factor(m$miRNA))
}
m <- as.data.table(m)
if(is.null(m$log_kd)){
m <- .aggregateSiteInfo(as.data.table(m))
return(as.data.frame(m, stringsAsFactor=TRUE))
}
if(keepSiteInfo){
m_type_table <- .aggregateSiteInfo(m)
}
for(col in c("ORF", "p3.score", "type")) if(is.null(m[[col]])) m[[col]] <- 0L
if(!is.null(m$miRNA)){
m <- m[,c("miRNA","transcript","ORF","log_kd","p3.score","type")]
}else{
m <- m[,c("transcript","ORF","log_kd","p3.score","type")]
}
m <- as.data.table(m)
m[, ORF:=as.integer(ORF)]
m[, log_kd:=-log_kd/1000]
m <- m[log_kd>0]
m$p3.score <- ifelse(m$type == "non-canonical" , 0L, m$p3.score)
m$p3.score[m$p3.score>max(p3.range)] <- as.integer(max(p3.range))
m$p3.score[m$p3.score<min(p3.range)] <- 0L
m$N <- 1 / (1 + exp(-1 * (log(a) + m$log_kd + log(c)*m$ORF + p3*m$p3.score) ))
m$log_kd <- NULL
m$N_bg <- 1 / (1 + exp(-1 * (log(a) + log(c)*m$ORF) ))
m <- as.data.frame(rowsum(as.matrix(m[,c("N","N_bg")]), group=m$transcript))
m <- data.frame( transcript=as.factor(row.names(m)),
repression=log2(1+exp(b)*m$N_bg) - log2(1 + exp(b)*m$N))
if(!is.null(ll) && nrow(m) > 1){
m <- merge(m,ll,by = "transcript", all.x = TRUE)
# get the utr score
m$utr.length <- log10(m$utr.length)
m$utr.length[is.infinite(m$utr.length) | is.na(m$utr.length)] <- 0
qu_un <- m[!duplicated(m$transcript),"utr.length"]
qu <- quantile(qu_un, probs = c(0.05,0.95), na.rm = TRUE)
m$utr_score <- (m$utr.length - qu[1]) / (qu[2] - qu[1])
m$utr_score[is.na(m$utr_score)] <- 0
# get the orf score
if(sum(m$orf.length, na.rm = TRUE) > 0){
m$orf.length <- log10(m$orf.length)
m$orf.length[is.infinite(m$orf.length) | is.na(m$orf.length)] <- 0
qu_un <- m[!duplicated(m$transcript),"orf.length"]
qu <- quantile(qu_un, probs = c(0.05,0.95), na.rm = TRUE)
m$orf_score <- (m$orf.length - qu[1]) / (qu[2] - qu[1])
m$orf_score[is.na(m$orf_score)] <- 0
}else{
m$orf_score <- 0
}
m$repression <- m$repression + coef_utr*m$utr_score*m$repression +
coef_orf*m$orf_score*m$repression
m <- subset(m, select = - c(orf.length,utr.length,utr_score,orf_score))
}
if(toInt) m$repression <- as.integer(round(1000*m$repression))
m$repression <- ifelse(m$repression >= 0, 0, m$repression)
if(keepSiteInfo){
if(!is.null(m$miRNA)){
m <- merge(m, m_type_table, by=c("transcript","miRNA"), all=TRUE)
}else{
m <- merge(m, m_type_table, by=c("transcript"), all=TRUE)
}
m$miRNA <- NULL
}
m
}
.aggregateSiteInfo <- function(m) {
hasORF <- !is.null(m$ORF)
if(is.null(m$ORF)) m[,ORF:=0]
if(is.null(m$miRNA)){
if(any(m$ORF==0)) {
sites <- dcast(m[ORF==0,.(N=.N), by=c("transcript","type")],
formula=transcript~type, value.var="N", fill=0L )
} else {
sites <- dcast(m[,.(N=.N), by=c("transcript","type")],
formula=transcript~type, value.var="N", fill=0L )
cols <- names(sites)[vapply(sites, is.numeric, logical(1))]
sites[, (cols):=0L]
}
if(hasORF) {
if(any(m$ORF)==1) {
sites_ORF <- dcast( m[ORF==1,.(N=.N), by=c("transcript","type")],
formula=transcript~type, value.var="N", fill=0L )
} else {
sites_ORF <- sites[, "transcript"]
}
}
} else{
if(any(m$ORF==0)) {
sites <- dcast( m[ORF==0,.(N=.N), by=c("transcript","miRNA","type")],
formula=transcript+miRNA~type, value.var="N",
fill=0L )
} else {
sites <- dcast(m[,.(N=.N), by=c("transcript", "miRNA", "type")],
formula=transcript+miRNA~type, value.var="N", fill=0L )
cols <- names(sites)[vapply(sites, is.numeric, logical(1))]
sites[, (cols):=0L]
}
if(hasORF) {
if(any(m$ORF)==1) {
sites_ORF <- dcast( m[ORF==1,.(N=.N), by=c("transcript","miRNA","type")],
formula=transcript+miRNA~type, value.var="N",
fill=0L )
} else {
sites_ORF <- sites[, c("transcript", "miRNA")]
}
}
}
ind_bulged <- grep("bulged", names(sites))
if(length(ind_bulged)>0) {
ind_nc <- c(ind_bulged, grep("non-canonical", names(sites)))
sites[,"non-canonical":= rowSums(.SD, na.rm = TRUE), .SDcols = ind_nc]
sites[, grep("bulged", names(sites)):=NULL]
}
ind_wobbled <- grep("wobbled", names(sites))
if(length(ind_wobbled)>0) {
ind_nc2 <- c(ind_wobbled, grep("non-canonical", names(sites)))
sites[,"non-canonical":= rowSums(.SD, na.rm = TRUE), .SDcols = ind_nc2]
sites[, grep("wobbled", names(sites)):=NULL]
}
if(hasORF) {
ind_bulged_orf <- grep("bulged", names(sites_ORF))
if(length(ind_bulged_orf)>0) {
ind_nc_orf <- c(ind_bulged_orf, grep("non-canonical", names(sites_ORF)))
sites_ORF[,"non-canonical":= rowSums(.SD, na.rm = TRUE), .SDcols = ind_nc_orf]
sites_ORF[, grep("bulged", names(sites_ORF)):=NULL]
}
ind_wobbled_orf <- grep("wobbled", names(sites_ORF))
if(length(ind_wobbled_orf)>0) {
ind_nc2_orf <- c(ind_wobbled_orf, grep("non-canonical", names(sites_ORF)))
sites_ORF[,"non-canonical":= rowSums(.SD, na.rm = TRUE), .SDcols = ind_nc2_orf]
sites_ORF[, grep("wobbled", names(sites_ORF)):=NULL]
}
}
ind_6mer <- grep("6mer", names(sites))
if(length(ind_6mer)>0) {
sites[, "6mer" := rowSums(.SD, na.rm=TRUE), .SDcols = ind_6mer]
}
ind_7mer <- grep("7mer", names(sites))
if(length(ind_7mer)>0) {
sites[, "7mer" := rowSums(.SD, na.rm=TRUE), .SDcols = ind_7mer]
}
for(col in setdiff(c("8mer", "7mer", "6mer", "non-canonical"), names(sites))){
sites[, (col):=0L]
}
if(hasORF) {
cols <- names(sites_ORF)[vapply(sites_ORF, is.numeric, logical(1))]
if(length(cols)==0) {
sites_ORF[, ORF.canonical:=0L]
sites_ORF[, ORF.nonCanonical:=0L]
} else {
sites_ORF[, "ORF.canonical":=rowSums(.SD, na.rm=TRUE), .SDcols = cols[which(cols != "non-canonical")]]
sites_ORF[, "ORF.nonCanonical":=rowSums(.SD, na.rm=TRUE), .SDcols = cols[which(cols == "non-canonical")]]
sites_ORF[, (cols):=NULL]
}
}
if(is.null(m$miRNA)) {
if(hasORF) {
cols <- c("transcript", "8mer", "7mer", "6mer", "non-canonical",
"ORF.canonical","ORF.nonCanonical")
sites <- merge(sites, sites_ORF, by="transcript", all=TRUE)
}
else {
cols <- c("transcript", "8mer", "7mer", "6mer", "non-canonical")
}
} else{
if(hasORF) {
cols <- c("transcript", "miRNA", "8mer", "7mer", "6mer", "non-canonical",
"ORF.canonical","ORF.nonCanonical")
sites <- merge(sites, sites_ORF, by=c("transcript", "miRNA"), all=TRUE)
} else {
cols <- c("transcript", "miRNA", "8mer", "7mer", "6mer", "non-canonical")
}
}
for(i in names(sites)){
sites[is.na(get(i)), (i):=0L]
if(is.numeric(sites[[i]])) sites[[i]] <- as.integer(sites[[i]])
}
cols <- intersect(cols, colnames(sites))
return(sites[,..cols])
}
.datatable.aware = TRUE
ETHZ-INS/scanMiR documentation built on April 16, 2024, noon
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Defines functions .aggregateSiteInfo .aggregate_miRNA aggregateMatches
Documented in aggregateMatches
#' aggregateMatches
#'
#' Aggregates miRNA binding sites with log_kd values to predict transcript
#' repression. See the vignette for more detail.
#'
#' @param m A GRanges or data.frame of matches as returned by `findSeedMatches`.
#' @param a The relative concentration of unbound AGO-miRNA complexes.
#' @param b Factor specifying the additional repression by a single bound AGO.
#' @param c Penalty for sites that are found within the ORF region.
#' @param p3 Factor specifying additional repression due to 3p alignment.
#' @param coef_utr Factor specifying additional repression due to UTR length.
#' @param coef_orf Factor specifying additional repression due to ORF length.
#' @param p3.range Range used for 3p alignment.
#' @param keepSiteInfo Logical; whether to return information about site types
#' (default = TRUE). Ignored if `m` does not contain `log_kd` values
#' @param toInt Logical; whether to convert repression scores to integers
#' (default = FALSE).
#' @param BP Pass `BiocParallel::MulticoreParam(ncores, progressbar=TRUE)` to
#' enable multithreading. Note that in addition, `aggregateMatches` uses the
#' \link{data.table} package, which is often set to use multi-threading by
#' default (which would be multiplied by threads determined by `BP`). See
#' \code{\link[data.table]{setDTthreads}} for more information.
#'
#' @return a data.frame containing aggregated repression values and/or
#' information about the numbers and types of matches
#' @export
#' @importFrom stats quantile
#' @importFrom data.table as.data.table .N := dcast rbindlist .SD
#'
#' @examples
#' # we create mock RNA sequences and seeds:
#' seqs <- getRandomSeq(n=10)
#'
#' # load sample KdModel
#' data(SampleKdModel)
#'
#' # find matches
#' matches <- findSeedMatches(seqs, SampleKdModel)
#'
#' # aggregate matches
#' aggregateMatches(matches)
aggregateMatches <- function(m, a=0.007726 , b=0.5735, c=0.1810, p3=0.051,
coef_utr=0, coef_orf=0, p3.range=c(3L,8L),
keepSiteInfo = TRUE, toInt=FALSE, BP=NULL){
if(is.null(BP)) BP <- BiocParallel::SerialParam()
ll <- NULL # length info
if(is(m,"GRanges")){
if(length(m)==0) stop("Your search did not result in any match.")
if(!is.null(ll <- metadata(m)$tx_info)){
ll <- ll[,c("ORF.length", "UTR.length")]
colnames(ll) <- tolower(colnames(ll))
ll$transcript <- row.names(ll)
}
m$transcript <- as.factor(seqnames(m))
m <- mcols(m)
if(!is.null(m$miRNA)) m$miRNA <- droplevels(as.factor(m$miRNA))
m <- as.data.frame(m)
}else{
if(nrow(m)==0) stop("Your search did not result in any match.")
if(!is.null(ll <- attr(m, "tx_info"))){
ll <- ll[,c("ORF.length", "UTR.length")]
colnames(ll) <- tolower(colnames(ll))
ll$transcript <- row.names(ll)
}
}
if(!is.null(m$miRNA)){
m <- split(m, m$miRNA)
m <- bplapply(m, BPPARAM=BP, FUN=function(x){
.aggregate_miRNA(x, ll=ll, a=a, b=b, c=c, p3=p3,coef_utr = coef_utr,
coef_orf = coef_orf, keepSiteInfo = keepSiteInfo,
toInt=toInt, p3.range=p3.range)
})
m <- as.data.frame(data.table::rbindlist(m, use.names=TRUE, fill=TRUE,
idcol = "miRNA"))
m$miRNA <- as.factor(m$miRNA)
for(f in colnames(m)){
if(is.numeric(m[[f]])) m[[f]][is.na(m[[f]])] <- 0L
}
}else{
m <- .aggregate_miRNA(m, ll=ll, a=a, b=b, c=c, p3=p3,coef_utr = coef_utr,
coef_orf = coef_orf, keepSiteInfo = keepSiteInfo,
toInt=toInt, p3.range=p3.range)
}
return(m)
}
.aggregate_miRNA <- function(m, ll = NULL, a=0.007726, b=0.5735, c=0.1810,
p3=0.051, coef_utr = -0.17106,
coef_orf = -0.21546, p3.range=c(3L,8L),
keepSiteInfo = FALSE, toInt=FALSE){
if(is(m,"GRanges")){
m$transcript <- as.factor(seqnames(m))
m <- mcols(m)
if(!is.null(m$miRNA)) m$miRNA <- droplevels(as.factor(m$miRNA))
}
m <- as.data.table(m)
if(is.null(m$log_kd)){
m <- .aggregateSiteInfo(as.data.table(m))
return(as.data.frame(m, stringsAsFactor=TRUE))
}
if(keepSiteInfo){
m_type_table <- .aggregateSiteInfo(m)
}
for(col in c("ORF", "p3.score", "type")) if(is.null(m[[col]])) m[[col]] <- 0L
if(!is.null(m$miRNA)){
m <- m[,c("miRNA","transcript","ORF","log_kd","p3.score","type")]
}else{
m <- m[,c("transcript","ORF","log_kd","p3.score","type")]
}
m <- as.data.table(m)
m[, ORF:=as.integer(ORF)]
m[, log_kd:=-log_kd/1000]
m <- m[log_kd>0]
m$p3.score <- ifelse(m$type == "non-canonical" , 0L, m$p3.score)
m$p3.score[m$p3.score>max(p3.range)] <- as.integer(max(p3.range))
m$p3.score[m$p3.score<min(p3.range)] <- 0L
m$N <- 1 / (1 + exp(-1 * (log(a) + m$log_kd + log(c)*m$ORF + p3*m$p3.score) ))
m$log_kd <- NULL
m$N_bg <- 1 / (1 + exp(-1 * (log(a) + log(c)*m$ORF) ))
m <- as.data.frame(rowsum(as.matrix(m[,c("N","N_bg")]), group=m$transcript))
m <- data.frame( transcript=as.factor(row.names(m)),
repression=log2(1+exp(b)*m$N_bg) - log2(1 + exp(b)*m$N))
if(!is.null(ll) && nrow(m) > 1){
m <- merge(m,ll,by = "transcript", all.x = TRUE)
# get the utr score
m$utr.length <- log10(m$utr.length)
m$utr.length[is.infinite(m$utr.length) | is.na(m$utr.length)] <- 0
qu_un <- m[!duplicated(m$transcript),"utr.length"]
qu <- quantile(qu_un, probs = c(0.05,0.95), na.rm = TRUE)
m$utr_score <- (m$utr.length - qu[1]) / (qu[2] - qu[1])
m$utr_score[is.na(m$utr_score)] <- 0
# get the orf score
if(sum(m$orf.length, na.rm = TRUE) > 0){
m$orf.length <- log10(m$orf.length)
m$orf.length[is.infinite(m$orf.length) | is.na(m$orf.length)] <- 0
qu_un <- m[!duplicated(m$transcript),"orf.length"]
qu <- quantile(qu_un, probs = c(0.05,0.95), na.rm = TRUE)
m$orf_score <- (m$orf.length - qu[1]) / (qu[2] - qu[1])
m$orf_score[is.na(m$orf_score)] <- 0
}else{
m$orf_score <- 0
}
m$repression <- m$repression + coef_utr*m$utr_score*m$repression +
coef_orf*m$orf_score*m$repression
m <- subset(m, select = - c(orf.length,utr.length,utr_score,orf_score))
}
if(toInt) m$repression <- as.integer(round(1000*m$repression))
m$repression <- ifelse(m$repression >= 0, 0, m$repression)
if(keepSiteInfo){
if(!is.null(m$miRNA)){
m <- merge(m, m_type_table, by=c("transcript","miRNA"), all=TRUE)
}else{
m <- merge(m, m_type_table, by=c("transcript"), all=TRUE)
}
m$miRNA <- NULL
}
m
}
.aggregateSiteInfo <- function(m) {
hasORF <- !is.null(m$ORF)
if(is.null(m$ORF)) m[,ORF:=0]
if(is.null(m$miRNA)){
if(any(m$ORF==0)) {
sites <- dcast(m[ORF==0,.(N=.N), by=c("transcript","type")],
formula=transcript~type, value.var="N", fill=0L )
} else {
sites <- dcast(m[,.(N=.N), by=c("transcript","type")],
formula=transcript~type, value.var="N", fill=0L )
cols <- names(sites)[vapply(sites, is.numeric, logical(1))]
sites[, (cols):=0L]
}
if(hasORF) {
if(any(m$ORF)==1) {
sites_ORF <- dcast( m[ORF==1,.(N=.N), by=c("transcript","type")],
formula=transcript~type, value.var="N", fill=0L )
} else {
sites_ORF <- sites[, "transcript"]
}
}
} else{
if(any(m$ORF==0)) {
sites <- dcast( m[ORF==0,.(N=.N), by=c("transcript","miRNA","type")],
formula=transcript+miRNA~type, value.var="N",
fill=0L )
} else {
sites <- dcast(m[,.(N=.N), by=c("transcript", "miRNA", "type")],
formula=transcript+miRNA~type, value.var="N", fill=0L )
cols <- names(sites)[vapply(sites, is.numeric, logical(1))]
sites[, (cols):=0L]
}
if(hasORF) {
if(any(m$ORF)==1) {
sites_ORF <- dcast( m[ORF==1,.(N=.N), by=c("transcript","miRNA","type")],
formula=transcript+miRNA~type, value.var="N",
fill=0L )
} else {
sites_ORF <- sites[, c("transcript", "miRNA")]
}
}
}
ind_bulged <- grep("bulged", names(sites))
if(length(ind_bulged)>0) {
ind_nc <- c(ind_bulged, grep("non-canonical", names(sites)))
sites[,"non-canonical":= rowSums(.SD, na.rm = TRUE), .SDcols = ind_nc]
sites[, grep("bulged", names(sites)):=NULL]
}
ind_wobbled <- grep("wobbled", names(sites))
if(length(ind_wobbled)>0) {
ind_nc2 <- c(ind_wobbled, grep("non-canonical", names(sites)))
sites[,"non-canonical":= rowSums(.SD, na.rm = TRUE), .SDcols = ind_nc2]
sites[, grep("wobbled", names(sites)):=NULL]
}
if(hasORF) {
ind_bulged_orf <- grep("bulged", names(sites_ORF))
if(length(ind_bulged_orf)>0) {
ind_nc_orf <- c(ind_bulged_orf, grep("non-canonical", names(sites_ORF)))
sites_ORF[,"non-canonical":= rowSums(.SD, na.rm = TRUE), .SDcols = ind_nc_orf]
sites_ORF[, grep("bulged", names(sites_ORF)):=NULL]
}
ind_wobbled_orf <- grep("wobbled", names(sites_ORF))
if(length(ind_wobbled_orf)>0) {
ind_nc2_orf <- c(ind_wobbled_orf, grep("non-canonical", names(sites_ORF)))
sites_ORF[,"non-canonical":= rowSums(.SD, na.rm = TRUE), .SDcols = ind_nc2_orf]
sites_ORF[, grep("wobbled", names(sites_ORF)):=NULL]
}
}
ind_6mer <- grep("6mer", names(sites))
if(length(ind_6mer)>0) {
sites[, "6mer" := rowSums(.SD, na.rm=TRUE), .SDcols = ind_6mer]
}
ind_7mer <- grep("7mer", names(sites))
if(length(ind_7mer)>0) {
sites[, "7mer" := rowSums(.SD, na.rm=TRUE), .SDcols = ind_7mer]
}
for(col in setdiff(c("8mer", "7mer", "6mer", "non-canonical"), names(sites))){
sites[, (col):=0L]
}
if(hasORF) {
cols <- names(sites_ORF)[vapply(sites_ORF, is.numeric, logical(1))]
if(length(cols)==0) {
sites_ORF[, ORF.canonical:=0L]
sites_ORF[, ORF.nonCanonical:=0L]
} else {
sites_ORF[, "ORF.canonical":=rowSums(.SD, na.rm=TRUE), .SDcols = cols[which(cols != "non-canonical")]]
sites_ORF[, "ORF.nonCanonical":=rowSums(.SD, na.rm=TRUE), .SDcols = cols[which(cols == "non-canonical")]]
sites_ORF[, (cols):=NULL]
}
}
if(is.null(m$miRNA)) {
if(hasORF) {
cols <- c("transcript", "8mer", "7mer", "6mer", "non-canonical",
"ORF.canonical","ORF.nonCanonical")
sites <- merge(sites, sites_ORF, by="transcript", all=TRUE)
}
else {
cols <- c("transcript", "8mer", "7mer", "6mer", "non-canonical")
}
} else{
if(hasORF) {
cols <- c("transcript", "miRNA", "8mer", "7mer", "6mer", "non-canonical",
"ORF.canonical","ORF.nonCanonical")
sites <- merge(sites, sites_ORF, by=c("transcript", "miRNA"), all=TRUE)
} else {
cols <- c("transcript", "miRNA", "8mer", "7mer", "6mer", "non-canonical")
}
}
for(i in names(sites)){
sites[is.na(get(i)), (i):=0L]
if(is.numeric(sites[[i]])) sites[[i]] <- as.integer(sites[[i]])
}
cols <- intersect(cols, colnames(sites))
return(sites[,..cols])
}
.datatable.aware = TRUE
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