Merge pull request #106 from GoekeLab/bambu-devel

Update documentations in master branch

Former-commit-id: 91eae31

.Rbuildignore

@@ -2,3 +2,5 @@
 ^\.Rproj\.user$
 ^packrat/
 ^\.Rprofile$
+^doc$
+^Meta$

.gitignore

@@ -40,3 +40,5 @@ vignettes/*.pdf
 .Renviron
 packrat/lib*/
 .Rproj.user
+doc
+Meta

DESCRIPTION

@@ -1,10 +1,10 @@
 Package: bambu
 Type: Package
 Title: Reference-guided isoform reconstruction and quantification for long read RNA-Seq data
-Version: 0.9.0
-Authors@R: c(person("Jonathan Goeke", "Developer", role = "aut",
-                     email = "[email protected]"),
-              person("Ying Chen", "Developer", role = "cre",email = "chen_ying@gis.a-star.edu.sg"))
+Version: 0.1.0
+Authors@R: c(person("Ying Chen", "Developer", role = "cre",email = "[email protected]"),
+             person("Jonathan Goeke", "Developer", role = "aut",
+                     email = "gokej@gis.a-star.edu.sg"))
 Description: Multi-sample transcript discovery and quantification using long read RNA-Seq data. 
 License: GPL-3 
 Encoding: UTF-8

R/abundance_quantification.R

@@ -2,26 +2,37 @@
 #' @title transcript_abundance_quantification
 #' @param method A string variable indicates the whether a one-step or two-step approach will be used. See \code{Details}
 #' for details on one-step and two-step approach.
-#' @param read_classDT A \code{data.table} with columns
+#' @param readClassDt A \code{data.table} with columns
 #' @importFrom BiocParallel bplapply
 #' @noRd
-abundance_quantification <- function(read_classDT,ncore = 1,
-                                     bias_correction = TRUE,
+abundance_quantification <- function(readClassDt,ncore = 1,
+                                     bias = TRUE,
                                      maxiter = 20000,
-                                     conv.control = 10^(-8)){
-  gene_sidList <- unique(read_classDT$gene_sid)
-
-  bpParameters <- BiocParallel::bpparam()
-  bpParameters$workers <- ncore
-
+                                     conv = 10^(-8)){
+  gene_sidList <- unique(readClassDt$gene_sid)
+
+    if(ncore == 1){
+
+      emResultsList <- lapply(as.list(gene_sidList),
+                                              run_parallel,
+                                              conv = conv,
+                                              bias = bias,
+                                              maxiter = maxiter,
+                                              readClassDt = readClassDt)
+    }else{
+      bpParameters <- BiocParallel::bpparam()
+      bpParameters$workers <- ncore
+
+      emResultsList <- BiocParallel::bplapply(as.list(gene_sidList),
+                                              run_parallel,
+                                              conv = conv,
+                                              bias = bias,
+                                              maxiter = maxiter,
+                                              readClassDt = readClassDt,
+                                              BPPARAM=bpParameters)
+    }
+
 
-  emResultsList <- BiocParallel::bplapply(as.list(gene_sidList),
-                                          run_parallel,
-                                          conv.control = conv.control,
-                                          bias_correction = bias_correction,
-                                          maxiter = maxiter,
-                                          read_classDT = read_classDT,
-                                          BPPARAM=bpParameters)
 
 
   estimates <- list(do.call('rbind',lapply(1:length(emResultsList), function(x) emResultsList[[x]][[1]])),
@@ -30,8 +41,8 @@ abundance_quantification <- function(read_classDT,ncore = 1,
   return(estimates)
 }
 
-run_parallel <- function(g,conv.control,bias_correction,maxiter, read_classDT){
-  tmp <- read_classDT[gene_sid==g]
+run_parallel <- function(g,conv,bias,maxiter, readClassDt){
+  tmp <- readClassDt[gene_sid==g]
   if((nrow(tmp)==1)){
     out <- list(data.table(tx_sid = tmp$tx_sid,
                                estimates = tmp$nobs,
@@ -58,12 +69,12 @@ run_parallel <- function(g,conv.control,bias_correction,maxiter, read_classDT){
     est_output <- emWithL1(X = as.matrix(a_mat),
                           Y = n.obs,
                           lambda = lambda,
-                          d = bias_correction,
+                          d = bias,
                           maxiter = maxiter,
-                          conv = conv.control)
+                          conv = conv)
     t_est <- as.numeric(t(est_output[["theta"]]))
 
-    if(bias_correction){
+    if(bias){
       b_est <- as.numeric(t(est_output[["b"]]))
     }else{
       b_est <- rep(0,ncol(a_mat))

R/annotationFunctions.R

@@ -34,39 +34,52 @@ prepareAnnotations <- function(txdb) {
 }
 
 
-#' Prepare annotations from gtf
-#' @title prepare annotations from gtf file
-#' @param gtf.file A string variable indicates the path to a gtf file.
-#' @param organism as described in \code{\link{makeTxDbFromGFF}}.
-#' @param dataSource as described in \code{\link{makeTxDbFromGFF}}.
-#' @param taxonomyId as described in \code{\link{makeTxDbFromGFF}}.
-#' @param chrominfo	as described in \code{\link{makeTxDbFromGFF}}.
-#' @param miRBaseBuild as described in \code{\link{makeTxDbFromGFF}}.
-#' @param metadata as described in \code{\link{makeTxDbFromGFF}}.
-#' @param dbxrefTag as described in \code{\link{makeTxDbFromGFF}}.
-#' @param ... see \code{\link{makeTxDbFromGFF}}.
-#' @return A \code{\link{GrangesList}} object
+#' Prepare annotation granges object from GTF file 
+#' @title Prepare annotation granges object from GTF file  into a GRangesList object
+#' @param file a GTF file
+#' @return grlist a \code{\link{GRangesList}} object, unlike \code\link{readFromGTF}}, 
+#' this function finds out the equivalence classes between the transcripts, 
+#' with \code{\link{mcols}} data having three columns:
+#' \itemize{
+#'   \item TXNAME specifying prefix for new gene Ids (genePrefix.number), defaults to empty
+#'   \item GENEID indicating whether filter to remove read classes which are a subset of known transcripts(), defaults to TRUE
+#'   \item eqClass specifying minimun read count to consider a read class valid in a sample, defaults to 2
+#'   }
+#' 
 #' @export
-prepareAnnotationsFromGTF <- function(gtf.file, dataSource=NA,
-                                     organism="Homo sapiens",
-                                     taxonomyId=NA,
-                                     chrominfo=NULL,
-                                     miRBaseBuild=NA,
-                                     metadata=NULL,
-                                     dbxrefTag,...){
-  return(prepareAnnotations(GenomicFeatures::makeTxDbFromGFF(gtf.file, format = "gtf",
-                                                             organism = organism,
-                                                             dataSource = dataSource,
-                                                             taxonomyId = taxonomyId,
-                                                             chrominfo = chrominfo,
-                                                             miRBaseBuild = miRBaseBuild,
-                                                             metadata = metadata,
-                                                             dbxrefTag = dbxrefTag
-  )))
+prepareAnnotationsFromGTF <- function(file){
+  if (missing(file)){
+    stop('A GTF file is required.')
+  }else{
+    data <- read.delim(file,header=FALSE,comment.char='#')
+    colnames(data) <- c("seqname","source","type","start","end","score","strand","frame","attribute")
+    data <- data[data$type=='exon',]
+    data$strand[data$strand=='.'] <- '*'
+    data$GENEID = gsub('gene_id (.*?);.*','\\1',data$attribute)
+    data$TXNAME=gsub('.*transcript_id (.*?);.*', '\\1',data$attribute)
+    data$exon_rank=as.integer(gsub('.*exon_number (.*?);.*', '\\1',data$attribute))
+    geneData=unique(data[,c('TXNAME', 'GENEID')])
+    grlist <- makeGRangesListFromDataFrame(
+      data[,c('seqname', 'start','end','strand','exon_rank','TXNAME')],split.field='TXNAME',keep.extra.columns = TRUE)
+
+    unlistedExons <- unlist(grlist, use.names = FALSE)
+    partitioning <- PartitioningByEnd(cumsum(elementNROWS(grlist)), names=NULL)
+    txIdForReorder <- togroup(PartitioningByWidth(grlist))
+    unlistedExons <- unlistedExons[order(txIdForReorder, unlistedExons$exon_rank)] #'exonsByTx' is always sorted by exon rank, not by strand, make sure that this is the case here
+    unlistedExons$exon_endRank <- unlist(sapply(elementNROWS(grlist),seq,to=1), use.names=FALSE)
+    unlistedExons <- unlistedExons[order(txIdForReorder, start(unlistedExons))]
+    #    mcols(unlistedExons) <- mcols(unlistedExons)[,c('exon_rank','exon_endRank')]
+    grlist <- relist(unlistedExons, partitioning)
+    minEqClasses <- getMinimumEqClassByTx(grlist)
+    mcols(grlist) <- DataFrame(geneData[(match(names(grlist), geneData$TXNAME)),])
+    mcols(grlist)$eqClass <- minEqClasses$eqClass[match(names(grlist),minEqClasses$queryTxId)]
+  }
+  return (grlist)
 }
 
 
 
+
 #' Get minimum equivalent class by Transcript
 #' @param exonsByTranscripts exonsByTranscripts
 #' @noRd