R/ImportMethods.R
In charles-plessy/CAGEr: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining
Defines functions
import.CAGEscanMolecule
import.CTSS
import.bedCTSS
import.bedScore
import.bedmolecule
import.bam.ctss
bam2CTSS
import.bam
moleculesGR2CTSS
loadFileIntoGPos
coerceInBSgenome
addCTSScolumn
toCTSSdt
checkFilesExist
Documented in
bam2CTSS
coerceInBSgenome
import.bam
import.bam.ctss
import.bedCTSS
import.bedmolecule
import.bedScore
import.CAGEscanMolecule
import.CTSS
loadFileIntoGPos
moleculesGR2CTSS
#' @include CAGEr.R CAGEexp.R ClusteringMethods.R CTSS.R GetMethods.R SetMethods.R
#' @name getCTSS
#'
#' @title Reading CAGE data from input file(s) and detecting TSSs
#'
#' @description Reads input CAGE datasets into CAGEr object, constructs CAGE
#' transcriptions start sites (CTSSs) and counts number of CAGE tags supporting every
#' CTSS in each input experiment. See \code{\link{inputFilesType}} for details on
#' the supported input formats. Preprocessing and quality filtering of input CAGE
#' tags, as well as correction of CAGE-specific 'G' nucleotide addition bias can be
#' also performed before constructing TSSs.
#'
#' @param object A [`CAGEexp`] object.
#'
#' @param sequencingQualityThreshold Only CAGE tags with average sequencing quality
#' \code{>= sequencingQualityThreshold} and mapping quality \code{>=
#' mappingQualityThreshold} are kept. Used only if \code{inputFileType(object)
#' == "bam"} or \code{inputFileType(object) == "bamPairedEnd"}, \emph{i.e} when
#' input files are BAM files of aligned sequenced CAGE tags, otherwise ignored. If
#' there are no sequencing quality values in the BAM file (\emph{e.g.} HeliScope
#' single molecule sequencer does not return sequencing qualities) all reads will by
#' default have this value set to -1. Since the default value of
#' \code{sequencingQualityThreshold} is 10, all the reads will consequently be
#' discarded. To avoid this behaviour and keep all sequenced reads set
#' \code{sequencingQualityThreshold} to -1 when processing data without sequencing
#' qualities. If there is no information on mapping quality in the BAM file
#' (\emph{e.g.} software used to align CAGE tags to the referent genome does not
#' provide mapping quality) the \code{mappingQualityThreshold} parameter is ignored.
#' In case of paired-end sequencing BAM file (i.e. \code{inputFileType(object) ==
#' "bamPairedEnd"}) only the first mate of the properly paired reads (i.e. the five
#' prime end read) will be read and subject to specified thresholds.
#'
#' @param mappingQualityThreshold See sequencingQualityThreshold.
#'
#' @param removeFirstG Logical, should the first nucleotide of the CAGE tag be removed
#' in case it is a G and it does not map to the referent genome (\emph{i.e.} it is a
#' mismatch). Used only if \code{inputFileType(object) == "bam"} or
#' \code{inputFileType(object) == "bamPairedEnd"}, \emph{i.e} when input files are
#' BAM files of aligned sequenced CAGE tags, otherwise ignored. See Details.
#'
#' @param correctSystematicG Logical, should the systematic correction of the first G
#' nucleotide be performed for the positions where there is a G in the CAGE tag and G
#' in the genome. This step is performed in addition to removing the first G of the
#' CAGE tags when it is a mismatch, \emph{i.e.} this option can only be used when
#' \code{removeFirstG = TRUE}, otherwise it is ignored. The frequency of adding a G
#' to CAGE tags is estimated from mismatch cases and used to systematically correct
#' the G addition for positions with G in the genome. Used only if
#' \code{inputFileType(object) == "bam"} or \code{inputFileType(object) ==
#' "bamPairedEnd"}, \emph{i.e} when input files are BAM files of aligned sequenced
#' CAGE tags, otherwise ignored. See Details.
#'
#' @param useMulticore Logical, should multicore be used.
#' \code{useMulticore = TRUE} has no effect on non-Unix-like platforms.
#'
#' @param nrCores Number of cores to use when \code{useMulticore = TRUE}
#' (set to \code{NULL} to use all detected cores).
#'
#' @details In the CAGE experimental protocol an additional G nucleotide is often attached
#' to the 5' end of the tag by the template-free activity of the reverse transcriptase used
#' to prepare cDNA (Harbers and Carninci, Nature Methods 2005). In cases where there is a
#' G at the 5' end of the CAGE tag that does not map to the corresponding genome sequence,
#' it can confidently be considered spurious and should be removed from the tag to avoid
#' misannotating actual TSS. Thus, setting \code{removeFirstG = TRUE} is highly recommended.
#'
#' However, when there is a G both at the beginning of the CAGE tag and in the genome, it is
#' not clear whether the original CAGE tag really starts at this position or the G nucleotide
#' was added later in the experimental protocol. To systematically correct CAGE tags mapping
#' at such positions, a general frequency of adding a G to CAGE tags can be calculated from
#' mismatch cases and applied to estimate the number of CAGE tags that have G added and
#' should actually start at the next nucleotide/position. The option \code{correctSystematicG}
#' is an implementation of the correction algorithm described in Carninci \emph{et al.},
#' Nature Genetics 2006, Supplementary Information section 3-e.
#'
#' @return Returns the object, in which the `tagCountMatrix` experiment will be
#' occupied by a [`RangedSummarizedExperiment`] containing the expression data
#' as a `DataFrame` of `Rle` integers, and the CTSS coordinates as genomic
#' ranges in a [`CTSS`] object. The expression data can be retrieved with
#' the [`CTSStagCountDF`] function. In addition, the library sizes are
#' calculated and stored in the object's sample data (see [`librarySizes`]).
#'
#' @references
#'
#' Harbers and Carninci (2005) Tag-based approaches for transcriptome research and genome
#' annotation, \emph{Nature Methods} \bold{2}(7):495-502.
#'
#' Carninci \emph{et al.} (2006) Genome-wide analysis of mammalian promoter architecture and
#' evolution, \emph{Nature Genetics} \bold{38}(7):626-635.
#'
#' @author Vanja Haberle
#'
#' @seealso \code{\link{inputFilesType}}, \code{\link{librarySizes}}.
#'
#' @family CAGEr object modifiers
#'
#' @examples
#' library(BSgenome.Drerio.UCSC.danRer7)
#'
#' pathsToInputFiles <- system.file("extdata", c("Zf.unfertilized.egg.chr17.ctss",
#' "Zf.30p.dome.chr17.ctss", "Zf.prim6.rep1.chr17.ctss"), package="CAGEr")
#'
#' labels <- paste("sample", seq(1,3,1), sep = "")
#'
#' myCAGEexp <- new("CAGEexp", genomeName = "BSgenome.Drerio.UCSC.danRer7",
#' inputFiles = pathsToInputFiles, inputFilesType = "ctss", sampleLabels = labels)
#'
#' myCAGEexp <- getCTSS(myCAGEexp)
#'
#' @docType methods
#'
#' @importFrom S4Vectors DataFrame
#' @importFrom S4Vectors Rle
#' @export
setGeneric( "getCTSS"
, function( object
, sequencingQualityThreshold = 10, mappingQualityThreshold = 20
, removeFirstG = TRUE, correctSystematicG = TRUE
, useMulticore = FALSE, nrCores = NULL)
standardGeneric("getCTSS"))
checkFilesExist <- function(paths) {
for (f in paths)
if (isFALSE(grepl("^http", f)))
if (! file.exists(f)) stop("Could not locate input file ", f)
}
#' toCTSSdt
#'
#' Non-exported private helper function
#'
#' @return Returns a \code{data.table} with TSS indicated in the \dQuote{chr},
#' \dQuote{pos} and \dQuote{strand} columns, and a score column named with
#' the sample label.
#'
#' @noRd
#' @importFrom data.table data.table
#' @importFrom data.table setkeyv
#' @importFrom data.table setnames
toCTSSdt <- function(reads.GRanges, sample.label) {
reads.GRanges.plus <- reads.GRanges[strand(reads.GRanges) == "+"]
reads.GRanges.minus <- reads.GRanges[strand(reads.GRanges) == "-"]
gr2ctssdf <- function(gr, strand)
data.frame( chr = as.character(seqnames(gr))
, pos = if (strand == "+") start(gr) else end(gr)
, strand = strand
, stringsAsFactors = F)
CTSS.plus <- gr2ctssdf(reads.GRanges.plus, "+")
CTSS.minus <-gr2ctssdf(reads.GRanges.minus, "-")
CTSS <- rbind(CTSS.plus, CTSS.minus)
CTSS$tag_count <- 1L
CTSS <- .byCtss(data.table(CTSS), "tag_count", sum)
setnames(CTSS, c("chr", "pos", "strand", sample.label))
setkeyv(CTSS, c("chr", "pos", "strand"))
CTSS
}
addCTSScolumn <- function(CTSS.all.samples, CTSS) {
if(is.null(CTSS.all.samples))
return(CTSS)
merge(CTSS.all.samples, CTSS, all.x = TRUE, all.y = TRUE)
}
#' coerceInBSgenome
#'
#' A private (non-exported) function to discard any range that is
#' not compatible with the CAGEr object's BSgenome.
#'
#' @param gr The genomic ranges to coerce.
#' @param genome The name of a BSgenome package, which must me installed,
#' or \code{NULL} to skip coercion.
#'
#' @return A GRanges object in which every range is guaranteed to be compatible
#' with the given BSgenome object. The sequnames of the GRanges are also set
#' accordingly to the BSgenome.
#'
#' @importFrom GenomeInfoDb seqinfo
#' @importFrom GenomeInfoDb seqlengths
#' @importFrom GenomeInfoDb seqlevels seqlevels<-
#' @importFrom GenomeInfoDb seqnames
#' @importFrom S4Vectors %in%
coerceInBSgenome <- function(gr, genome) {
if (is.null(genome)) return(gr)
genome <- getRefGenome(genome)
gr <- gr[seqnames(gr) %in% seqnames(genome)]
gr <- gr[! end(gr) > seqlengths(genome)[as.character(seqnames(gr))]]
seqlevels(gr) <- seqlevels(genome)
seqinfo(gr) <- seqinfo(genome)
gr
}
#' loadFileIntoGPos
#'
#' A private (non-exported) function to load from each file format supported by CAGEr
#'
#' @param filepath The path to the file to load.
#' @param filetype The type of the file
#' @param sequencingQualityThreshold See getCTSS().
#' @param mappingQualityThreshold See getCTSS().
#' @param removeFirstG See getCTSS().
#' @param correctSystematicG See getCTSS().
#' @param genome See coerceInBSgenome().
#'
#' @return A [GPos()] object where the score represents the number of CAGE tags
#' starting on that nucleotide.
#'
#' @seealso import.CTSS
#' @family loadFileIntoGPos
loadFileIntoGPos <- function( filepath
, filetype = c( "bam", "bamPairedEnd"
, "bed", "bedctss", "bedScore"
, "CAGEscanMolecule", "ctss")
, sequencingQualityThreshold
, mappingQualityThreshold
, removeFirstG
, correctSystematicG
, genome) {
if (missing(filetype)) stop("Please specify the file type.")
filetype <- match.arg(filetype)
switch( filetype
, bam = import.bam.ctss( filepath = filepath
, filetype = "bam"
, sequencingQualityThreshold = sequencingQualityThreshold
, mappingQualityThreshold = mappingQualityThreshold
, removeFirstG = removeFirstG
, correctSystematicG = correctSystematicG
, genome = genome)
, bamPairedEnd = import.bam.ctss( filepath = filepath
, filetype = "bamPairedEnd"
, sequencingQualityThreshold = sequencingQualityThreshold
, mappingQualityThreshold = mappingQualityThreshold
, removeFirstG = removeFirstG
, correctSystematicG = correctSystematicG
, genome = genome)
, bed = import.bedmolecule(filepath)
, bedScore = import.bedScore(filepath)
, bedctss = import.bedCTSS(filepath)
, CAGEscanMolecule = import.CAGEscanMolecule(filepath)
, ctss = import.CTSS(filepath))
}
#' moleculesGR2CTSS
#'
#' Calculates CTSS positions from a GenomicRanges object where each element
#' represents a single molecule.
#'
#' @param gr A [GRanges] object.
#'
#' @return Returns a [GRanges] object.
#'
#' @family loadFileIntoGPos
#'
#' @importFrom S4Vectors Rle
#'
#' @examples
#' gr <- GenomicRanges::GRanges("chr1", IRanges::IRanges(1, 10), c("+", "-", "+"))
#' CAGEr:::moleculesGR2CTSS(gr)
moleculesGR2CTSS <- function(gr) {
tb <- table(promoters(gr, 0, 1))
gp <- as(names(tb), "UnstitchedGPos")
score(gp) <- Rle(as.integer(tb))
gp
}
#' import.bam
#'
#' Imports CTSS data from a BAM file.
#'
#' @param filepath The path to the BAM file.
#' @param filetype bam or bamPairedEnd.
#' @param sequencingQualityThreshold See getCTSS().
#' @param mappingQualityThreshold See getCTSS().
#'
#' @family loadFileIntoGPos
#'
#' @importFrom Rsamtools bamFlag<-
#' @importFrom Rsamtools ScanBamParam
#' @importFrom Rsamtools scanBamFlag
#' @importFrom GenomicAlignments readGAlignments
#' @importFrom GenomicAlignments qwidth
#'
#' @examples
#' # TODO: add exmaple file
#' # import.bam(system.file("extdata", "example.bam", package = "CAGEr"))
import.bam <- function( filepath
, filetype
, sequencingQualityThreshold = 10
, mappingQualityThreshold = 20) {
param <- ScanBamParam( what = c("seq", "qual")
, flag = scanBamFlag(isUnmappedQuery = FALSE)
, mapqFilter = mappingQualityThreshold)
if (filetype == "bamPairedEnd")
bamFlag(param) <- scanBamFlag( isUnmappedQuery = FALSE
, isProperPair = TRUE
, isFirstMateRead = TRUE)
bam <- readGAlignments(filepath, param = param)
message("\t-> Filtering out low quality reads...")
# We need to loop on qualities because there is a hard limit on the length.
# See <https://support.bioconductor.org/p/97993/>.
qual <- mcols(bam)$qual
start <- 1
chunksize <- 1e6
qual.avg <- vector(mode = "integer")
repeat {
if (start + chunksize <= length(qual)) {
end <- start + chunksize
} else {
end <- length(qual)
}
qual.avg <- c(qual.avg, as.integer(mean(as(qual[start:end], "IntegerList"))))
if (end == length(qual)) {
break
} else {
start <- end + 1
}
}
bam <- bam[qual.avg >= sequencingQualityThreshold]
gr <- GRanges(bam)
gr$read.length <- qwidth(bam)
gr
}
#' bam2CTSS
#'
#' Converts from BAM to CTSS
#'
#' Converts genomic ranges representing SAM/BAM alignments into a CTSS object.
#'
#' @param gr A \code{\link{GRanges}} object returned by \code{\link{import.bam}}.
#' @param removeFirstG See getCTSS().
#' @param correctSystematicG See getCTSS().
#' @param genome See coerceInBSgenome().
#'
#' @return Returns a \code{\link{CTSS}} object.
#'
#' @importFrom GenomeInfoDb bsgenomeName
#' @family loadFileIntoGPos
bam2CTSS <- function(gr, removeFirstG, correctSystematicG, genome) {
gr <- coerceInBSgenome(gr, genome)
if(removeFirstG == TRUE) {
message("\t-> Removing the first base of the reads if 'G' and not aligned to the genome...")
if (isFALSE(correctSystematicG)) {
gp <- .remove.added.G.CTSS(gr, genome, correctSystematicG = FALSE)
} else {
# Using the old CAGEset function that returns a data.table
gp <- .remove.added.G(gr, genome, correctSystematicG = TRUE, "score")
return(gp)
}
} else {
gp <- CTSS(promoters(gr, 0, 1))
}
tb <- table(gp)
gp <- CTSS(names(tb), bsgenomeName = genome)
score(gp) <- Rle(unclass(tb))
gp
}
#' import.bam.ctss
#'
#' Imports CTSS data from a BAM file.
#'
#' @param filepath The path to the BAM file.
#' @param filetype bam or bamPairedEnd.
#' @param sequencingQualityThreshold See getCTSS().
#' @param mappingQualityThreshold See getCTSS().
#' @param removeFirstG See getCTSS().
#' @param correctSystematicG See getCTSS().
#' @param genome See coerceInBSgenome().
#'
#' @return Returns a [CTSS] object.
#'
#' @family loadFileIntoGPos
import.bam.ctss <- function( filepath, filetype, sequencingQualityThreshold
, mappingQualityThreshold, removeFirstG
, correctSystematicG, genome) {
bam <- import.bam( filepath = filepath
, filetype = filetype
, sequencingQualityThreshold = sequencingQualityThreshold
, mappingQualityThreshold = mappingQualityThreshold)
bam2CTSS( gr= bam
, removeFirstG = removeFirstG
, correctSystematicG = correctSystematicG
, genome = genome)
}
#' import.bedmolecule
#'
#' Imports a BED file where each line counts for one molecule in a
#' GRanges object where each line represents one nucleotide.
#'
#' @param filepath The path to the BED file.
#'
#' @return Returns a \code{\link{CTSS}} object.
#'
#' @family loadFileIntoGPos
#'
#' @importFrom rtracklayer import.bed
#' @importFrom S4Vectors Rle
#'
#' @examples
#' # TODO: add exmaple file
#' # import.BED(system.file("extdata", "example.bed", package = "CAGEr"))
import.bedmolecule <- function(filepath) {
moleculesGR2CTSS(rtracklayer::import.bed(filepath))
}
#' import.bedScore
#'
#' Imports a BED file where the score indicates a number of counts
#' for a given alignment.
#'
#' @param filepath The path to the BED file.
#'
#' @return A GRanges object where each line represents one nucleotide.
#'
#' @family loadFileIntoGPos
#'
#' @importFrom rtracklayer import.bed
#' @importFrom S4Vectors Rle
#'
#' @examples
#' # TODO: add exmaple file
#' # import.bedScore(system.file("extdata", "example.bed", package = "CAGEr"))
import.bedScore <- function(filepath) {
gr <- rtracklayer::import.bed(filepath)
df <- data.frame( p = as.character(promoters(gr, 0, 1))
, s = score(gr))
df <- rowsum(df$s, df$p)
gp <- as(rownames(df), "UnstitchedGPos")
score(gp) <- Rle(as.integer(df[,1]))
gp
}
#' import.bedCTSS
#'
#' Imports a BED file where each line represents a single base, with a score
#' counting the number of CAGE transcription start sites (CTSS).
#'
#' @return A GRanges object where each line represents one nucleotide.
#'
#' @param filepath The path to the BED file.
#'
#' @family loadFileIntoGPos
#'
#' @importFrom rtracklayer import.bed
#' @importFrom GenomeInfoDb sortSeqlevels
#' @importFrom GenomicRanges countOverlaps
#'
#' @examples
#' # TODO: add exmaple file
#' # import.BED(system.file("extdata", "example.bed", package = "CAGEr"))
import.bedCTSS <- function(filepath) {
gr <- rtracklayer::import.bed(filepath)
if (length(unique(gr)) != length(gr))
stop("Input must not contain the same range multiple times.")
if (any(countOverlaps(gr) != 1))
stop("Input must not contain overlapping ranges.")
seqlevels(gr) <- sortSeqlevels(seqlevels(gr))
mcols(gr) <- DataFrame(score = Rle(as.integer(score(gr))))
as(gr, "UnstitchedGPos")
}
#' import.CTSS
#'
#' Imports a "CTSS" file in a [GPos] object
#'
#' @param filepath The path to the "CTSS" file.
#'
#' Note that the format of the "CTSS" files handled in this function is not
#' the same as the FANTOM5 "CTSS" files (which are plain BED).
#'
#' @family loadFileIntoGPos
#'
#' @importFrom utils read.table
#'
#' @examples
#' CAGEr:::import.CTSS(system.file("extdata", "Zf.high.chr17.ctss", package = "CAGEr"))
import.CTSS <- function(filepath) {
CTSS <- read.table( file = filepath
, header = F
, sep = "\t"
, col.names = c("chr", "pos", "strand", "score")
, colClasses = c("character", "integer", "character", "integer"))
gp <- GPos( stitch = FALSE
, GRanges( seqnames = CTSS$chr
, ranges = IRanges(CTSS$pos, width = 1)
, strand = CTSS$strand))
score(gp) <- CTSS$score
gp
}
#' parseCAGEscanBlocksToGrangeTSS
#'
#' Parse a string describing a block in a CAGEscan molecule, as output by
#' the "CAGEscan 3.0" pipeline.
#'
#' @param blocks A character string representing a block in a CAGEscan molecule.
#'
#' @return A GRanges object representing a TSS.
#'
#' In CAGEscan molecules, blocks are separated by \sQuote{|}, \sQuote{,} or
#' \sQuote{;} for gap of coverage, splice junction (confident) and splice
#' junction (maybe) respectively. Strand is "+" if first coordinate is lower
#' than the second one, and "-" otherwise.
#'
#' @seealso import.CAGEscanMolecule
#'
#' @examples
#' myMolecule <- paste0( "chr11:66268633-66268693,"
#' , "chr11:66271796-66271869;"
#' , "chr11:66272156-66272252|"
#' , "chr11:66272364-66272460")
#' myFirstBlock <- sub("[,;|].*", "", myMolecule)
#'
#' CAGEr:::parseCAGEscanBlocksToGrangeTSS(myFirstBlock)
parseCAGEscanBlocksToGrangeTSS <- function (blocks) {
blocks <- strsplit(blocks, "[:-]")
chr <- unlist(lapply(blocks, `[[`, 1))
fst <- as.integer(unlist(lapply(blocks, `[[`, 2)))
snd <- as.integer(unlist(lapply(blocks, `[[`, 3)))
strand <- ifelse(fst < snd, "+", "-")
start <- pmin(fst, snd)
GPos(stitch=FALSE, GRanges(chr, IRanges(start, width = 1), strand))
}
#' import.CAGEscanMolecule
#'
#' Imports a CAGEscan \dQuote{molecule} file in a [`GRanges`] object
#'
#' @param filepath The path to the \dQuote{molecule} file.
#'
#' @seealso parseCAGEscanBlocksToGrangeTSS
#'
#' @importFrom GenomicRanges GRangesList
#'
#' @examples
#' # TODO import.CAGEscanMolecule(system.file("extdata", "example.molecule.txt", package = "CAGEr"))
#'
#' @importFrom data.table fread
import.CAGEscanMolecule <- function(filepath) {
molecules <- unname(unlist(data.table::fread(select = 9, paste( "grep -v \\#", filepath))))
molecules <- sub("[,;|].*", "", molecules)
parseCAGEscanBlocksToGrangeTSS(molecules)
}
#' @rdname getCTSS
setMethod( "getCTSS", "CAGEexp"
, function ( object
, sequencingQualityThreshold, mappingQualityThreshold
, removeFirstG, correctSystematicG
, useMulticore, nrCores) {
if(isFALSE(removeFirstG) & isTRUE(correctSystematicG))
stop("correctSystematicG can not be TRUE while removeFirstG is FALSE")
# Step 0: Test existance of each file before spending time loading them.
checkFilesExist(inputFiles(object))
# Step 1: Load each file as GRangesList where each GRange is a CTSS data.
l <- as.list(seq_along(inputFiles(object)))
l <- bplapply(l, function(i) {
message("\nReading in file: ", inputFiles(object)[i], "...")
gr <- loadFileIntoGPos( filepath = inputFiles(object)[i]
, filetype = inputFilesType(object)[i]
, sequencingQualityThreshold = sequencingQualityThreshold
, mappingQualityThreshold = mappingQualityThreshold
, removeFirstG = removeFirstG
, correctSystematicG = correctSystematicG
, genome = genomeName(object))
gr <- coerceInBSgenome(gr, genomeName(object))
l[[i]] <- sort(gr)
}, BPPARAM = CAGEr_Multicore(useMulticore, nrCores))
l <- GRangesList(l)
# Step 2: Create GPos representing all the nucleotides with CAGE counts in the list.
rowRanges <- sort(unique(unlist(l)))
mcols(rowRanges) <- NULL
# Step 3: Fold the GRangesList in a expression DataFrame of Rle-encoded counts.
assay <- DataFrame(V1 = Rle(rep(0L, length(rowRanges))))
expandRange <- function(global, local) {
x <- Rle(rep(0L, length(global)))
x[global %in% local] <- score(local)
x
}
for (i in seq_along(l))
assay[,i] <- expandRange(rowRanges, l[[i]])
rowRanges <- new("CTSS", rowRanges, bsgenomeName = genomeName(object))
colnames(assay) <- sampleLabels(object)
# Setp 4: Put the data in the appropriate slot of the MultiAssayExperiment.
CTSStagCountSE(object) <-
SummarizedExperiment( rowRanges = rowRanges
, assays = SimpleList(counts = assay))
# Step 5: update the sample metadata (colData).
librarySizes(object) <- unlist(lapply(CTSStagCountDF(object), sum))
# Setp 6: Return the modified object.
object
})
#' FANTOM5 human samples
#'
#' Lazy-loaded `data.frame` object, containing information about FANTOM5
#' libraries. Its use is described in more details in the vignette
#' \dQuote{Use of CAGE resources with CAGEr}.
#'
#' @format A `data.frame` with `sample`, `type`, `description`, `library_id`
#' and `data_url` columns.
#'
#' @family FANTOM data
"FANTOM5humanSamples"
#' FANTOM5 mouse samples
#'
#' Lazy-loaded `data.frame` object, containing information about FANTOM5
#' libraries. Its use is described in more details in the vignette
#' \dQuote{Use of CAGE resources with CAGEr}.
#'
#' @format A `data.frame` with `sample`, `type`, `description`, `library_id`
#' and `data_url` columns.
#'
#' @family FANTOM data
"FANTOM5mouseSamples"
#' #' importPublicData
#' #' @noRd
#' #' @importFrom utils data
#' #' @export
#'
#' setGeneric(
#' name="importPublicData",
#' def=function(source, dataset, group, sample){
#' standardGeneric("importPublicData")
#' }
#' )
#'
#' setMethod("importPublicData",
#' signature(source = "character", dataset = "character", sample = "character"),
#' function (source, dataset, group, sample){
#'
#' if(source == "ENCODE"){
#'
#' if("ENCODEprojectCAGE" %in% rownames(installed.packages()) == FALSE){
#' stop("Requested CAGE data package is not installed! Please install and load the ENCODEprojectCAGE package, which is available for download from http://promshift.genereg.net/CAGEr/PackageSource/.")
#' }else if(!("package:ENCODEprojectCAGE" %in% search())){
#' stop("Requested CAGE data package is not loaded! Please load the data package by calling 'library(ENCODEprojectCAGE)'")
#' }
#'
#' if(dataset[1] == "ENCODEtissueCAGEfly"){
#' genome.name <- "BSgenome.Dmelanogaster.UCSC.dm3"
#' ENCODEtissueCAGEfly <- NULL
#' data("ENCODEtissueCAGEfly", envir = environment())
#' if(group == "embryo"){
#' if(sample == "mixed_embryos_0-24hr"){
#'
#' ctssTable <- ENCODEtissueCAGEfly[["embryo"]]
#'
#' }else{
#' stop("Specified sample not valid! The dataset 'ENCODEtissueCAGEfly' containes only one sample named 'mixed_embryos_0-24hr'!")
#' }
#' }else{
#' stop("Specified group not valid! The dataset 'ENCODEtissueCAGEfly' containes only one group named 'embryo'!")
#' }
#'
#' }else{
#' genome.name <- "BSgenome.Hsapiens.UCSC.hg19"
#' ENCODEhumanCellLinesSamples <- NULL
#' data("ENCODEhumanCellLinesSamples", envir = environment())
#' info.df <- ENCODEhumanCellLinesSamples
#'
#' if(!(all(dataset %in% info.df$dataset))){
#' stop("Specified dataset(s) not found! Call data(ENCODEhumanCellLinesSamples) and check 'dataset' column for available ENCODE datasets!")
#' }
#' if(length(dataset) == 1){
#'
#' if(!(all(group %in% info.df[info.df$dataset == dataset,"group"]))){
#' stop("Some of the provided groups cannot be found in the specified dataset!")
#' }
#' if(length(group) == 1){
#' if(!(all(sample %in% info.df[info.df$group == group,"sample"]))){
#' stop("Some of the provided samples cannot be found in the specified group!")
#' }
#' }else if(length(group) == length(sample)){
#' if(!all(sapply(c(1:length(group)), function(x) {sample[x] %in% info.df[info.df$group == group[x],"sample"]}))){
#' stop("Provided 'group' and 'sample' do not match! Some of the provided samples cannot be found in the corresponding groups!")
#' }
#' }else{
#' stop("Number of elements in the 'group' must be either 1 or must match the number of elements in the 'sample'!")
#' }
#'
#'
#' }else if(length(dataset) == length(group)){
#' if(!all(sapply(c(1:length(dataset)), function(x) {group[x] %in% info.df[info.df$dataset == dataset[x],"group"]}))){
#' stop("Provided 'dataset' and 'group' do not match! Some of the provided groups cannot be found in the corresponding datasets!")
#' }
#' if(length(group) == length(sample)){
#' if(!all(sapply(c(1:length(group)), function(x) {sample[x] %in% info.df[info.df$group == group[x],"sample"]}))){
#' stop("Provided 'group' and 'sample' do not match! Some of the provided samples cannot be found in the corresponding groups!")
#' }
#' }else{
#' stop("Number of elements in the 'group' must match the number of elements in the 'sample'!")
#' }
#'
#'
#' }else{
#' stop("Number of elements in the 'dataset' must be either 1 or must match the number of elements in the 'group' and in the 'sample'!")
#' }
#'
#' data(list = dataset, envir = environment())
#'
#' if(length(unique(dataset))>1){
#'
#' for(i in 1:length(unique(dataset))){
#' dset <- get(unique(dataset)[i])
#' for(j in 1:length(unique(group[which(dataset == unique(dataset)[i])]))){
#' g <- unique(group[which(dataset == unique(dataset)[i])])[j]
#' ctss <- dset[[g]][, c("chr", "pos", "strand", sample[which((group == g) & (dataset == unique(dataset)[i]))])]
#' discard <- apply(ctss[, c(4:ncol(ctss)), drop = F], 1, function(x) {sum(x > 0)}) >= 1
#' ctss <- data.table(ctss[discard,])
#' setkeyv(ctss, cols = c("chr", "pos", "strand"))
#' if(i == 1 & j == 1){
#' ctssTable <- ctss
#' }else{
#' ctssTable <- merge(ctssTable, ctss, all.x = T, all.y = T)
#' }
#' }
#' }
#' ctssTable[is.na(ctssTable)] <- 0
#'
#' }else{
#'
#' selected.dataset <- get(dataset)
#' if(length(unique(group))>1){
#'
#' for(i in 1:length(unique(group))){
#' g <- unique(group)[i]
#' ctss <- selected.dataset[[g]][, c("chr", "pos", "strand", sample[which(group == g)])]
#' discard <- apply(ctss[, c(4:ncol(ctss)), drop = F], 1, function(x) {sum(x > 0)}) >= 1
#' ctss <- data.table(ctss[discard,])
#' setkeyv(ctss, cols = c("chr", "pos", "strand"))
#' if(i == 1){
#' ctssTable <- ctss
#' }else{
#' ctssTable <- merge(ctssTable, ctss, all.x = T, all.y = T)
#' }
#' }
#'
#' ctssTable[is.na(ctssTable)] <- 0
#'
#' }else{
#' ctssTable <- selected.dataset[[group]][,c("chr", "pos", "strand", sample)]
#' }
#' }
#'
#' ctssTable <- data.frame(ctssTable, stringsAsFactors = F, check.names = F)
#' }
#'
#'
#' }else if(source == "FANTOM3and4"){
#'
#' if("FANTOM3and4CAGE" %in% rownames(installed.packages()) == FALSE){
#' stop("Requested CAGE data package is not installed! Please install and load the FANTOM3and4CAGE package available from Bioconductor.")
#' }else if(!("package:FANTOM3and4CAGE" %in% search())){
#' stop("Requested CAGE data package is not loaded! Please load the data package by calling 'library(FANTOM3and4CAGE)'")
#' }
#' FANTOMhumanSamples <- FANTOMmouseSamples <- NULL
#' data("FANTOMhumanSamples", envir = environment())
#' info.df1 <- FANTOMhumanSamples
#' data("FANTOMmouseSamples", envir = environment())
#' info.df2 <- FANTOMmouseSamples
#'
#' if(!(all(dataset %in% info.df1$dataset) | all(dataset %in% info.df2$dataset))){
#' stop("Specified dataset(s) not found! Call data(FANTOMhumanSamples) and data(FANTOMmouseSamples) and check 'dataset' column for available ENCODE datasets!")
#' }
#' if(length(grep("human", dataset))>0){
#' genome.name <- "BSgenome.Hsapiens.UCSC.hg18"
#' info.df <- info.df1
#' }else if(length(grep("mouse", dataset))>0){
#' genome.name <- "BSgenome.Mmusculus.UCSC.mm9"
#' info.df <- info.df2
#' }
#'
#' if(length(dataset) == 1){
#'
#' if(!(all(group %in% info.df[info.df$dataset == dataset,"group"]))){
#' stop("Some of the provided groups cannot be found in the specified dataset!")
#' }
#' if(length(group) == 1){
#' if(!(all(sample %in% info.df[info.df$group == group,"sample"]))){
#' stop("Some of the provided samples cannot be found in the specified group!")
#' }
#' }else if(length(group) == length(sample)){
#' if(!all(sapply(c(1:length(group)), function(x) {sample[x] %in% info.df[info.df$group == group[x],"sample"]}))){
#' stop("Provided 'group' and 'sample' do not match! Some of the provided samples cannot be found in the corresponding groups!")
#' }
#' }else{
#' stop("Number of elements in the 'group' must be either 1 or must match the number of elements in the 'sample'!")
#' }
#'
#'
#' }else if(length(dataset) == length(group)){
#' if(!all(sapply(c(1:length(dataset)), function(x) {group[x] %in% info.df[info.df$dataset == dataset[x],"group"]}))){
#' stop("Provided 'dataset' and 'group' do not match! Some of the provided groups cannot be found in the corresponding datasets!")
#' }
#' if(length(group) == length(sample)){
#' if(!all(sapply(c(1:length(group)), function(x) {sample[x] %in% info.df[info.df$group == group[x],"sample"]}))){
#' stop("Provided 'group' and 'sample' do not match! Some of the provided samples cannot be found in the corresponding groups!")
#' }
#' }else{
#' stop("Number of elements in the 'group' must match the number of elements in the 'sample'!")
#' }
#'
#'
#' }else{
#' stop("Number of elements in the 'dataset' must be either 1 or must match the number of elements in the 'group' and in the 'sample'!")
#' }
#'
#' data(list = dataset, envir = environment())
#'
#' if(length(unique(dataset))>1){
#'
#' for(i in 1:length(unique(dataset))){
#' dset <- get(unique(dataset)[i])
#' for(j in 1:length(unique(group[which(dataset == unique(dataset)[i])]))){
#' g <- unique(group[which(dataset == unique(dataset)[i])])[j]
#' ctss <- dset[[g]][, c("chr", "pos", "strand", sample[which(group == g)])]
#' discard <- apply(ctss[, c(4:ncol(ctss)), drop = F], 1, function(x) {sum(x > 0)}) >= 1
#' ctss <- data.table(ctss[discard,])
#' setnames(ctss, c("chr", "pos", "strand", paste(g, sample[which(group == g)], sep = "__")))
#' setkeyv(ctss, cols = c("chr", "pos", "strand"))
#' if(i == 1 & j == 1){
#' ctssTable <- ctss
#' }else{
#' ctssTable <- merge(ctssTable, ctss, all.x = T, all.y = T)
#' }
#' }
#' }
#'
#' ctssTable[is.na(ctssTable)] <- 0
#'
#' }else{
#'
#' selected.dataset <- get(dataset)
#' if(length(unique(group))>1){
#'
#' for(i in 1:length(unique(group))){
#' g <- unique(group)[i]
#' ctss <- selected.dataset[[g]][, c("chr", "pos", "strand", sample[which(group == g)])]
#' discard <- apply(ctss[, c(4:ncol(ctss)), drop = F], 1, function(x) {sum(x > 0)}) >= 1
#' ctss <- data.table(ctss[discard,])
#' setnames(ctss, c("chr", "pos", "strand", paste(g, sample[which(group == g)], sep = "__")))
#' setkeyv(ctss, cols = c("chr", "pos", "strand"))
#' if(i == 1){
#' ctssTable <- ctss
#' }else{
#' ctssTable <- merge(ctssTable, ctss, all.x = T, all.y = T)
#' }
#' }
#'
#' ctssTable[is.na(ctssTable)] <- 0
#'
#' }else{
#' ctssTable <- selected.dataset[[group]][,c("chr", "pos", "strand", sample)]
#' setnames(ctssTable, c("chr", "pos", "strand", paste(group, sample, sep = "__")))
#' }
#' }
#'
#' ctssTable <- data.frame(ctssTable, stringsAsFactors = F, check.names = F)
#'
#'
#'
#' }else if (source == "FANTOM5"){
#'
#' if(length(dataset) != 1){
#' stop("For FANTOM5 only one dataset can be specified and it can be either 'human' or 'mouse'!")
#' }else if(!(dataset %in% c("human", "mouse"))){
#' stop("For FANTOM5, dataset can be either 'human' or 'mouse'!")
#' }
#' if(dataset == "human"){
#' FANTOM5humanSamples <- NULL
#' data("FANTOM5humanSamples", envir = environment())
#' samples.info <- FANTOM5humanSamples
#' genome.name <- "BSgenome.Hsapiens.UCSC.hg19"
#' }else if(dataset == "mouse"){
#' FANTOM5mouseSamples <- NULL
#' data("FANTOM5mouseSamples", envir = environment())
#' samples.info <- FANTOM5mouseSamples
#' genome.name <- "BSgenome.Mmusculus.UCSC.mm9"
#' }
#'
#' if(!(all(sample %in% samples.info$sample))){
#' stop(paste("Some sample names cannot be found for the specified dataset! Call data(FANTOM5", dataset, "Samples) and check the 'sample' column for valid sample names!", sep = ""))
#' }
#'
#'
#'
#' for(i in c(1:length(sample))){
#'
#' message("Fetching sample: ", sample[i], "...")
#' sample.url <- samples.info[samples.info$sample == sample[i], "data_url"]
#' con <- gzcon(url(paste(sample.url)))
#' ctss <- scan(con, what = list(character(), NULL, integer(), NULL, integer(), character()))
#' ctss.df <- data.table(chr = ctss[[1]], pos = ctss[[3]], strand = ctss[[6]], tagCount = ctss[[5]])
#' setnames(ctss.df, c("chr", "pos", "strand", sample[i]))
#' setkeyv(ctss.df, cols = c("chr", "pos", "strand"))
#' if(i == 1){
#' ctss.table <- ctss.df
#' }else{
#' message("Adding sample to CTSS table...\n")
#' ctss.table <- merge(ctss.table, ctss.df, all.x = T, all.y = T)
#' ctss.table[is.na(ctss.table)] <- 0
#' }
#'
#' }
#'
#' ctssTable <- data.frame(ctss.table, stringsAsFactors = F, check.names = F)
#'
#'
#' }else if (source == "ZebrafishDevelopment"){
#'
#' if("ZebrafishDevelopmentalCAGE" %in% rownames(installed.packages()) == FALSE){
#' stop("Requested CAGE data package is not installed! Please install and load the ZebrafishDevelopmentalCAGE package, which is available for download from http://promshift.genereg.net/CAGEr/PackageSource/.")
#' }else if(!("package:ZebrafishDevelopmentalCAGE" %in% search())){
#' stop("Requested CAGE data package is not loaded! Please load the data package by calling 'library(ZebrafishDevelopmentalCAGE)'")
#' }
#'
#' ZebrafishSamples <- NULL
#' data("ZebrafishSamples", envir = environment())
#' if(dataset == "ZebrafishCAGE"){
#' if(group == "development"){
#' if(!(all(sample %in% ZebrafishSamples$sample))){
#' stop("Some sample names cannot be found for the specified dataset! Call data(ZebrafishSamples) and check the 'sample' column for valid sample names!")
#' }else{
#' genome.name <- "BSgenome.Drerio.UCSC.danRer7"
#' ZebrafishCAGE <- NULL
#' data("ZebrafishCAGE", envir = environment())
#' ctssTable <- ZebrafishCAGE[["development"]][,c("chr", "pos", "strand", sample)]
#' ctssTable <- ctssTable[apply(ctssTable[,4:ncol(ctssTable),drop=FALSE], 1, function(x) {any(x>0)}),]
#' }
#' }else{
#' stop("Invalid group name! There is only one group in this dataset named 'development'.")
#' }
#' }else{
#' stop("Invalid dataset name! There is only one available dataset named 'ZebrafishCAGE'.")
#' }
#'
#'
#' }else{
#' stop("Currently only the following public CAGE data resources are supported: 'FANTOM5', 'FANTOM3and4', 'ENCODE', 'ZebrafishDevelopment'. Refer to CAGEr vignette on how to use those resources!")
#' }
#'
#' rownames(ctssTable) <- c(1:nrow(ctssTable))
#'
#' sample.labels <- colnames(ctssTable)[4:ncol(ctssTable)]
#' names(sample.labels) <- rainbow(n = length(sample.labels))
#' myCAGEset <- new("CAGEset", genomeName = genome.name, inputFiles = paste(source, sample.labels, sep = "__"), inputFilesType = source, sampleLabels = sample.labels)
#' myCAGEset@librarySizes <- as.integer(colSums(ctssTable[,4:ncol(ctssTable),drop=FALSE]))
#' myCAGEset@CTSScoordinates <- ctssTable[, c("chr", "pos", "strand")]
#' myCAGEset@tagCountMatrix <- ctssTable[,4:ncol(ctssTable),drop=FALSE]
#'
#' return(myCAGEset)
#'
#' }
#' )
charles-plessy/CAGEr documentation built on Nov. 4, 2023, 11:57 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions import.CAGEscanMolecule import.CTSS import.bedCTSS import.bedScore import.bedmolecule import.bam.ctss bam2CTSS import.bam moleculesGR2CTSS loadFileIntoGPos coerceInBSgenome addCTSScolumn toCTSSdt checkFilesExist
Documented in bam2CTSS coerceInBSgenome import.bam import.bam.ctss import.bedCTSS import.bedmolecule import.bedScore import.CAGEscanMolecule import.CTSS loadFileIntoGPos moleculesGR2CTSS
#' @include CAGEr.R CAGEexp.R ClusteringMethods.R CTSS.R GetMethods.R SetMethods.R
#' @name getCTSS
#'
#' @title Reading CAGE data from input file(s) and detecting TSSs
#'
#' @description Reads input CAGE datasets into CAGEr object, constructs CAGE
#' transcriptions start sites (CTSSs) and counts number of CAGE tags supporting every
#' CTSS in each input experiment. See \code{\link{inputFilesType}} for details on
#' the supported input formats. Preprocessing and quality filtering of input CAGE
#' tags, as well as correction of CAGE-specific 'G' nucleotide addition bias can be
#' also performed before constructing TSSs.
#'
#' @param object A [`CAGEexp`] object.
#'
#' @param sequencingQualityThreshold Only CAGE tags with average sequencing quality
#' \code{>= sequencingQualityThreshold} and mapping quality \code{>=
#' mappingQualityThreshold} are kept. Used only if \code{inputFileType(object)
#' == "bam"} or \code{inputFileType(object) == "bamPairedEnd"}, \emph{i.e} when
#' input files are BAM files of aligned sequenced CAGE tags, otherwise ignored. If
#' there are no sequencing quality values in the BAM file (\emph{e.g.} HeliScope
#' single molecule sequencer does not return sequencing qualities) all reads will by
#' default have this value set to -1. Since the default value of
#' \code{sequencingQualityThreshold} is 10, all the reads will consequently be
#' discarded. To avoid this behaviour and keep all sequenced reads set
#' \code{sequencingQualityThreshold} to -1 when processing data without sequencing
#' qualities. If there is no information on mapping quality in the BAM file
#' (\emph{e.g.} software used to align CAGE tags to the referent genome does not
#' provide mapping quality) the \code{mappingQualityThreshold} parameter is ignored.
#' In case of paired-end sequencing BAM file (i.e. \code{inputFileType(object) ==
#' "bamPairedEnd"}) only the first mate of the properly paired reads (i.e. the five
#' prime end read) will be read and subject to specified thresholds.
#'
#' @param mappingQualityThreshold See sequencingQualityThreshold.
#'
#' @param removeFirstG Logical, should the first nucleotide of the CAGE tag be removed
#' in case it is a G and it does not map to the referent genome (\emph{i.e.} it is a
#' mismatch). Used only if \code{inputFileType(object) == "bam"} or
#' \code{inputFileType(object) == "bamPairedEnd"}, \emph{i.e} when input files are
#' BAM files of aligned sequenced CAGE tags, otherwise ignored. See Details.
#'
#' @param correctSystematicG Logical, should the systematic correction of the first G
#' nucleotide be performed for the positions where there is a G in the CAGE tag and G
#' in the genome. This step is performed in addition to removing the first G of the
#' CAGE tags when it is a mismatch, \emph{i.e.} this option can only be used when
#' \code{removeFirstG = TRUE}, otherwise it is ignored. The frequency of adding a G
#' to CAGE tags is estimated from mismatch cases and used to systematically correct
#' the G addition for positions with G in the genome. Used only if
#' \code{inputFileType(object) == "bam"} or \code{inputFileType(object) ==
#' "bamPairedEnd"}, \emph{i.e} when input files are BAM files of aligned sequenced
#' CAGE tags, otherwise ignored. See Details.
#'
#' @param useMulticore Logical, should multicore be used.
#' \code{useMulticore = TRUE} has no effect on non-Unix-like platforms.
#'
#' @param nrCores Number of cores to use when \code{useMulticore = TRUE}
#' (set to \code{NULL} to use all detected cores).
#'
#' @details In the CAGE experimental protocol an additional G nucleotide is often attached
#' to the 5' end of the tag by the template-free activity of the reverse transcriptase used
#' to prepare cDNA (Harbers and Carninci, Nature Methods 2005). In cases where there is a
#' G at the 5' end of the CAGE tag that does not map to the corresponding genome sequence,
#' it can confidently be considered spurious and should be removed from the tag to avoid
#' misannotating actual TSS. Thus, setting \code{removeFirstG = TRUE} is highly recommended.
#'
#' However, when there is a G both at the beginning of the CAGE tag and in the genome, it is
#' not clear whether the original CAGE tag really starts at this position or the G nucleotide
#' was added later in the experimental protocol. To systematically correct CAGE tags mapping
#' at such positions, a general frequency of adding a G to CAGE tags can be calculated from
#' mismatch cases and applied to estimate the number of CAGE tags that have G added and
#' should actually start at the next nucleotide/position. The option \code{correctSystematicG}
#' is an implementation of the correction algorithm described in Carninci \emph{et al.},
#' Nature Genetics 2006, Supplementary Information section 3-e.
#'
#' @return Returns the object, in which the `tagCountMatrix` experiment will be
#' occupied by a [`RangedSummarizedExperiment`] containing the expression data
#' as a `DataFrame` of `Rle` integers, and the CTSS coordinates as genomic
#' ranges in a [`CTSS`] object. The expression data can be retrieved with
#' the [`CTSStagCountDF`] function. In addition, the library sizes are
#' calculated and stored in the object's sample data (see [`librarySizes`]).
#'
#' @references
#'
#' Harbers and Carninci (2005) Tag-based approaches for transcriptome research and genome
#' annotation, \emph{Nature Methods} \bold{2}(7):495-502.
#'
#' Carninci \emph{et al.} (2006) Genome-wide analysis of mammalian promoter architecture and
#' evolution, \emph{Nature Genetics} \bold{38}(7):626-635.
#'
#' @author Vanja Haberle
#'
#' @seealso \code{\link{inputFilesType}}, \code{\link{librarySizes}}.
#'
#' @family CAGEr object modifiers
#'
#' @examples
#' library(BSgenome.Drerio.UCSC.danRer7)
#'
#' pathsToInputFiles <- system.file("extdata", c("Zf.unfertilized.egg.chr17.ctss",
#' "Zf.30p.dome.chr17.ctss", "Zf.prim6.rep1.chr17.ctss"), package="CAGEr")
#'
#' labels <- paste("sample", seq(1,3,1), sep = "")
#'
#' myCAGEexp <- new("CAGEexp", genomeName = "BSgenome.Drerio.UCSC.danRer7",
#' inputFiles = pathsToInputFiles, inputFilesType = "ctss", sampleLabels = labels)
#'
#' myCAGEexp <- getCTSS(myCAGEexp)
#'
#' @docType methods
#'
#' @importFrom S4Vectors DataFrame
#' @importFrom S4Vectors Rle
#' @export
setGeneric( "getCTSS"
, function( object
, sequencingQualityThreshold = 10, mappingQualityThreshold = 20
, removeFirstG = TRUE, correctSystematicG = TRUE
, useMulticore = FALSE, nrCores = NULL)
standardGeneric("getCTSS"))
checkFilesExist <- function(paths) {
for (f in paths)
if (isFALSE(grepl("^http", f)))
if (! file.exists(f)) stop("Could not locate input file ", f)
}
#' toCTSSdt
#'
#' Non-exported private helper function
#'
#' @return Returns a \code{data.table} with TSS indicated in the \dQuote{chr},
#' \dQuote{pos} and \dQuote{strand} columns, and a score column named with
#' the sample label.
#'
#' @noRd
#' @importFrom data.table data.table
#' @importFrom data.table setkeyv
#' @importFrom data.table setnames
toCTSSdt <- function(reads.GRanges, sample.label) {
reads.GRanges.plus <- reads.GRanges[strand(reads.GRanges) == "+"]
reads.GRanges.minus <- reads.GRanges[strand(reads.GRanges) == "-"]
gr2ctssdf <- function(gr, strand)
data.frame( chr = as.character(seqnames(gr))
, pos = if (strand == "+") start(gr) else end(gr)
, strand = strand
, stringsAsFactors = F)
CTSS.plus <- gr2ctssdf(reads.GRanges.plus, "+")
CTSS.minus <-gr2ctssdf(reads.GRanges.minus, "-")
CTSS <- rbind(CTSS.plus, CTSS.minus)
CTSS$tag_count <- 1L
CTSS <- .byCtss(data.table(CTSS), "tag_count", sum)
setnames(CTSS, c("chr", "pos", "strand", sample.label))
setkeyv(CTSS, c("chr", "pos", "strand"))
CTSS
}
addCTSScolumn <- function(CTSS.all.samples, CTSS) {
if(is.null(CTSS.all.samples))
return(CTSS)
merge(CTSS.all.samples, CTSS, all.x = TRUE, all.y = TRUE)
}
#' coerceInBSgenome
#'
#' A private (non-exported) function to discard any range that is
#' not compatible with the CAGEr object's BSgenome.
#'
#' @param gr The genomic ranges to coerce.
#' @param genome The name of a BSgenome package, which must me installed,
#' or \code{NULL} to skip coercion.
#'
#' @return A GRanges object in which every range is guaranteed to be compatible
#' with the given BSgenome object. The sequnames of the GRanges are also set
#' accordingly to the BSgenome.
#'
#' @importFrom GenomeInfoDb seqinfo
#' @importFrom GenomeInfoDb seqlengths
#' @importFrom GenomeInfoDb seqlevels seqlevels<-
#' @importFrom GenomeInfoDb seqnames
#' @importFrom S4Vectors %in%
coerceInBSgenome <- function(gr, genome) {
if (is.null(genome)) return(gr)
genome <- getRefGenome(genome)
gr <- gr[seqnames(gr) %in% seqnames(genome)]
gr <- gr[! end(gr) > seqlengths(genome)[as.character(seqnames(gr))]]
seqlevels(gr) <- seqlevels(genome)
seqinfo(gr) <- seqinfo(genome)
gr
}
#' loadFileIntoGPos
#'
#' A private (non-exported) function to load from each file format supported by CAGEr
#'
#' @param filepath The path to the file to load.
#' @param filetype The type of the file
#' @param sequencingQualityThreshold See getCTSS().
#' @param mappingQualityThreshold See getCTSS().
#' @param removeFirstG See getCTSS().
#' @param correctSystematicG See getCTSS().
#' @param genome See coerceInBSgenome().
#'
#' @return A [GPos()] object where the score represents the number of CAGE tags
#' starting on that nucleotide.
#'
#' @seealso import.CTSS
#' @family loadFileIntoGPos
loadFileIntoGPos <- function( filepath
, filetype = c( "bam", "bamPairedEnd"
, "bed", "bedctss", "bedScore"
, "CAGEscanMolecule", "ctss")
, sequencingQualityThreshold
, mappingQualityThreshold
, removeFirstG
, correctSystematicG
, genome) {
if (missing(filetype)) stop("Please specify the file type.")
filetype <- match.arg(filetype)
switch( filetype
, bam = import.bam.ctss( filepath = filepath
, filetype = "bam"
, sequencingQualityThreshold = sequencingQualityThreshold
, mappingQualityThreshold = mappingQualityThreshold
, removeFirstG = removeFirstG
, correctSystematicG = correctSystematicG
, genome = genome)
, bamPairedEnd = import.bam.ctss( filepath = filepath
, filetype = "bamPairedEnd"
, sequencingQualityThreshold = sequencingQualityThreshold
, mappingQualityThreshold = mappingQualityThreshold
, removeFirstG = removeFirstG
, correctSystematicG = correctSystematicG
, genome = genome)
, bed = import.bedmolecule(filepath)
, bedScore = import.bedScore(filepath)
, bedctss = import.bedCTSS(filepath)
, CAGEscanMolecule = import.CAGEscanMolecule(filepath)
, ctss = import.CTSS(filepath))
}
#' moleculesGR2CTSS
#'
#' Calculates CTSS positions from a GenomicRanges object where each element
#' represents a single molecule.
#'
#' @param gr A [GRanges] object.
#'
#' @return Returns a [GRanges] object.
#'
#' @family loadFileIntoGPos
#'
#' @importFrom S4Vectors Rle
#'
#' @examples
#' gr <- GenomicRanges::GRanges("chr1", IRanges::IRanges(1, 10), c("+", "-", "+"))
#' CAGEr:::moleculesGR2CTSS(gr)
moleculesGR2CTSS <- function(gr) {
tb <- table(promoters(gr, 0, 1))
gp <- as(names(tb), "UnstitchedGPos")
score(gp) <- Rle(as.integer(tb))
gp
}
#' import.bam
#'
#' Imports CTSS data from a BAM file.
#'
#' @param filepath The path to the BAM file.
#' @param filetype bam or bamPairedEnd.
#' @param sequencingQualityThreshold See getCTSS().
#' @param mappingQualityThreshold See getCTSS().
#'
#' @family loadFileIntoGPos
#'
#' @importFrom Rsamtools bamFlag<-
#' @importFrom Rsamtools ScanBamParam
#' @importFrom Rsamtools scanBamFlag
#' @importFrom GenomicAlignments readGAlignments
#' @importFrom GenomicAlignments qwidth
#'
#' @examples
#' # TODO: add exmaple file
#' # import.bam(system.file("extdata", "example.bam", package = "CAGEr"))
import.bam <- function( filepath
, filetype
, sequencingQualityThreshold = 10
, mappingQualityThreshold = 20) {
param <- ScanBamParam( what = c("seq", "qual")
, flag = scanBamFlag(isUnmappedQuery = FALSE)
, mapqFilter = mappingQualityThreshold)
if (filetype == "bamPairedEnd")
bamFlag(param) <- scanBamFlag( isUnmappedQuery = FALSE
, isProperPair = TRUE
, isFirstMateRead = TRUE)
bam <- readGAlignments(filepath, param = param)
message("\t-> Filtering out low quality reads...")
# We need to loop on qualities because there is a hard limit on the length.
# See <https://support.bioconductor.org/p/97993/>.
qual <- mcols(bam)$qual
start <- 1
chunksize <- 1e6
qual.avg <- vector(mode = "integer")
repeat {
if (start + chunksize <= length(qual)) {
end <- start + chunksize
} else {
end <- length(qual)
}
qual.avg <- c(qual.avg, as.integer(mean(as(qual[start:end], "IntegerList"))))
if (end == length(qual)) {
break
} else {
start <- end + 1
}
}
bam <- bam[qual.avg >= sequencingQualityThreshold]
gr <- GRanges(bam)
gr$read.length <- qwidth(bam)
gr
}
#' bam2CTSS
#'
#' Converts from BAM to CTSS
#'
#' Converts genomic ranges representing SAM/BAM alignments into a CTSS object.
#'
#' @param gr A \code{\link{GRanges}} object returned by \code{\link{import.bam}}.
#' @param removeFirstG See getCTSS().
#' @param correctSystematicG See getCTSS().
#' @param genome See coerceInBSgenome().
#'
#' @return Returns a \code{\link{CTSS}} object.
#'
#' @importFrom GenomeInfoDb bsgenomeName
#' @family loadFileIntoGPos
bam2CTSS <- function(gr, removeFirstG, correctSystematicG, genome) {
gr <- coerceInBSgenome(gr, genome)
if(removeFirstG == TRUE) {
message("\t-> Removing the first base of the reads if 'G' and not aligned to the genome...")
if (isFALSE(correctSystematicG)) {
gp <- .remove.added.G.CTSS(gr, genome, correctSystematicG = FALSE)
} else {
# Using the old CAGEset function that returns a data.table
gp <- .remove.added.G(gr, genome, correctSystematicG = TRUE, "score")
return(gp)
}
} else {
gp <- CTSS(promoters(gr, 0, 1))
}
tb <- table(gp)
gp <- CTSS(names(tb), bsgenomeName = genome)
score(gp) <- Rle(unclass(tb))
gp
}
#' import.bam.ctss
#'
#' Imports CTSS data from a BAM file.
#'
#' @param filepath The path to the BAM file.
#' @param filetype bam or bamPairedEnd.
#' @param sequencingQualityThreshold See getCTSS().
#' @param mappingQualityThreshold See getCTSS().
#' @param removeFirstG See getCTSS().
#' @param correctSystematicG See getCTSS().
#' @param genome See coerceInBSgenome().
#'
#' @return Returns a [CTSS] object.
#'
#' @family loadFileIntoGPos
import.bam.ctss <- function( filepath, filetype, sequencingQualityThreshold
, mappingQualityThreshold, removeFirstG
, correctSystematicG, genome) {
bam <- import.bam( filepath = filepath
, filetype = filetype
, sequencingQualityThreshold = sequencingQualityThreshold
, mappingQualityThreshold = mappingQualityThreshold)
bam2CTSS( gr= bam
, removeFirstG = removeFirstG
, correctSystematicG = correctSystematicG
, genome = genome)
}
#' import.bedmolecule
#'
#' Imports a BED file where each line counts for one molecule in a
#' GRanges object where each line represents one nucleotide.
#'
#' @param filepath The path to the BED file.
#'
#' @return Returns a \code{\link{CTSS}} object.
#'
#' @family loadFileIntoGPos
#'
#' @importFrom rtracklayer import.bed
#' @importFrom S4Vectors Rle
#'
#' @examples
#' # TODO: add exmaple file
#' # import.BED(system.file("extdata", "example.bed", package = "CAGEr"))
import.bedmolecule <- function(filepath) {
moleculesGR2CTSS(rtracklayer::import.bed(filepath))
}
#' import.bedScore
#'
#' Imports a BED file where the score indicates a number of counts
#' for a given alignment.
#'
#' @param filepath The path to the BED file.
#'
#' @return A GRanges object where each line represents one nucleotide.
#'
#' @family loadFileIntoGPos
#'
#' @importFrom rtracklayer import.bed
#' @importFrom S4Vectors Rle
#'
#' @examples
#' # TODO: add exmaple file
#' # import.bedScore(system.file("extdata", "example.bed", package = "CAGEr"))
import.bedScore <- function(filepath) {
gr <- rtracklayer::import.bed(filepath)
df <- data.frame( p = as.character(promoters(gr, 0, 1))
, s = score(gr))
df <- rowsum(df$s, df$p)
gp <- as(rownames(df), "UnstitchedGPos")
score(gp) <- Rle(as.integer(df[,1]))
gp
}
#' import.bedCTSS
#'
#' Imports a BED file where each line represents a single base, with a score
#' counting the number of CAGE transcription start sites (CTSS).
#'
#' @return A GRanges object where each line represents one nucleotide.
#'
#' @param filepath The path to the BED file.
#'
#' @family loadFileIntoGPos
#'
#' @importFrom rtracklayer import.bed
#' @importFrom GenomeInfoDb sortSeqlevels
#' @importFrom GenomicRanges countOverlaps
#'
#' @examples
#' # TODO: add exmaple file
#' # import.BED(system.file("extdata", "example.bed", package = "CAGEr"))
import.bedCTSS <- function(filepath) {
gr <- rtracklayer::import.bed(filepath)
if (length(unique(gr)) != length(gr))
stop("Input must not contain the same range multiple times.")
if (any(countOverlaps(gr) != 1))
stop("Input must not contain overlapping ranges.")
seqlevels(gr) <- sortSeqlevels(seqlevels(gr))
mcols(gr) <- DataFrame(score = Rle(as.integer(score(gr))))
as(gr, "UnstitchedGPos")
}
#' import.CTSS
#'
#' Imports a "CTSS" file in a [GPos] object
#'
#' @param filepath The path to the "CTSS" file.
#'
#' Note that the format of the "CTSS" files handled in this function is not
#' the same as the FANTOM5 "CTSS" files (which are plain BED).
#'
#' @family loadFileIntoGPos
#'
#' @importFrom utils read.table
#'
#' @examples
#' CAGEr:::import.CTSS(system.file("extdata", "Zf.high.chr17.ctss", package = "CAGEr"))
import.CTSS <- function(filepath) {
CTSS <- read.table( file = filepath
, header = F
, sep = "\t"
, col.names = c("chr", "pos", "strand", "score")
, colClasses = c("character", "integer", "character", "integer"))
gp <- GPos( stitch = FALSE
, GRanges( seqnames = CTSS$chr
, ranges = IRanges(CTSS$pos, width = 1)
, strand = CTSS$strand))
score(gp) <- CTSS$score
gp
}
#' parseCAGEscanBlocksToGrangeTSS
#'
#' Parse a string describing a block in a CAGEscan molecule, as output by
#' the "CAGEscan 3.0" pipeline.
#'
#' @param blocks A character string representing a block in a CAGEscan molecule.
#'
#' @return A GRanges object representing a TSS.
#'
#' In CAGEscan molecules, blocks are separated by \sQuote{|}, \sQuote{,} or
#' \sQuote{;} for gap of coverage, splice junction (confident) and splice
#' junction (maybe) respectively. Strand is "+" if first coordinate is lower
#' than the second one, and "-" otherwise.
#'
#' @seealso import.CAGEscanMolecule
#'
#' @examples
#' myMolecule <- paste0( "chr11:66268633-66268693,"
#' , "chr11:66271796-66271869;"
#' , "chr11:66272156-66272252|"
#' , "chr11:66272364-66272460")
#' myFirstBlock <- sub("[,;|].*", "", myMolecule)
#'
#' CAGEr:::parseCAGEscanBlocksToGrangeTSS(myFirstBlock)
parseCAGEscanBlocksToGrangeTSS <- function (blocks) {
blocks <- strsplit(blocks, "[:-]")
chr <- unlist(lapply(blocks, `[[`, 1))
fst <- as.integer(unlist(lapply(blocks, `[[`, 2)))
snd <- as.integer(unlist(lapply(blocks, `[[`, 3)))
strand <- ifelse(fst < snd, "+", "-")
start <- pmin(fst, snd)
GPos(stitch=FALSE, GRanges(chr, IRanges(start, width = 1), strand))
}
#' import.CAGEscanMolecule
#'
#' Imports a CAGEscan \dQuote{molecule} file in a [`GRanges`] object
#'
#' @param filepath The path to the \dQuote{molecule} file.
#'
#' @seealso parseCAGEscanBlocksToGrangeTSS
#'
#' @importFrom GenomicRanges GRangesList
#'
#' @examples
#' # TODO import.CAGEscanMolecule(system.file("extdata", "example.molecule.txt", package = "CAGEr"))
#'
#' @importFrom data.table fread
import.CAGEscanMolecule <- function(filepath) {
molecules <- unname(unlist(data.table::fread(select = 9, paste( "grep -v \\#", filepath))))
molecules <- sub("[,;|].*", "", molecules)
parseCAGEscanBlocksToGrangeTSS(molecules)
}
#' @rdname getCTSS
setMethod( "getCTSS", "CAGEexp"
, function ( object
, sequencingQualityThreshold, mappingQualityThreshold
, removeFirstG, correctSystematicG
, useMulticore, nrCores) {
if(isFALSE(removeFirstG) & isTRUE(correctSystematicG))
stop("correctSystematicG can not be TRUE while removeFirstG is FALSE")
# Step 0: Test existance of each file before spending time loading them.
checkFilesExist(inputFiles(object))
# Step 1: Load each file as GRangesList where each GRange is a CTSS data.
l <- as.list(seq_along(inputFiles(object)))
l <- bplapply(l, function(i) {
message("\nReading in file: ", inputFiles(object)[i], "...")
gr <- loadFileIntoGPos( filepath = inputFiles(object)[i]
, filetype = inputFilesType(object)[i]
, sequencingQualityThreshold = sequencingQualityThreshold
, mappingQualityThreshold = mappingQualityThreshold
, removeFirstG = removeFirstG
, correctSystematicG = correctSystematicG
, genome = genomeName(object))
gr <- coerceInBSgenome(gr, genomeName(object))
l[[i]] <- sort(gr)
}, BPPARAM = CAGEr_Multicore(useMulticore, nrCores))
l <- GRangesList(l)
# Step 2: Create GPos representing all the nucleotides with CAGE counts in the list.
rowRanges <- sort(unique(unlist(l)))
mcols(rowRanges) <- NULL
# Step 3: Fold the GRangesList in a expression DataFrame of Rle-encoded counts.
assay <- DataFrame(V1 = Rle(rep(0L, length(rowRanges))))
expandRange <- function(global, local) {
x <- Rle(rep(0L, length(global)))
x[global %in% local] <- score(local)
x
}
for (i in seq_along(l))
assay[,i] <- expandRange(rowRanges, l[[i]])
rowRanges <- new("CTSS", rowRanges, bsgenomeName = genomeName(object))
colnames(assay) <- sampleLabels(object)
# Setp 4: Put the data in the appropriate slot of the MultiAssayExperiment.
CTSStagCountSE(object) <-
SummarizedExperiment( rowRanges = rowRanges
, assays = SimpleList(counts = assay))
# Step 5: update the sample metadata (colData).
librarySizes(object) <- unlist(lapply(CTSStagCountDF(object), sum))
# Setp 6: Return the modified object.
object
})
#' FANTOM5 human samples
#'
#' Lazy-loaded `data.frame` object, containing information about FANTOM5
#' libraries. Its use is described in more details in the vignette
#' \dQuote{Use of CAGE resources with CAGEr}.
#'
#' @format A `data.frame` with `sample`, `type`, `description`, `library_id`
#' and `data_url` columns.
#'
#' @family FANTOM data
"FANTOM5humanSamples"
#' FANTOM5 mouse samples
#'
#' Lazy-loaded `data.frame` object, containing information about FANTOM5
#' libraries. Its use is described in more details in the vignette
#' \dQuote{Use of CAGE resources with CAGEr}.
#'
#' @format A `data.frame` with `sample`, `type`, `description`, `library_id`
#' and `data_url` columns.
#'
#' @family FANTOM data
"FANTOM5mouseSamples"
#' #' importPublicData
#' #' @noRd
#' #' @importFrom utils data
#' #' @export
#'
#' setGeneric(
#' name="importPublicData",
#' def=function(source, dataset, group, sample){
#' standardGeneric("importPublicData")
#' }
#' )
#'
#' setMethod("importPublicData",
#' signature(source = "character", dataset = "character", sample = "character"),
#' function (source, dataset, group, sample){
#'
#' if(source == "ENCODE"){
#'
#' if("ENCODEprojectCAGE" %in% rownames(installed.packages()) == FALSE){
#' stop("Requested CAGE data package is not installed! Please install and load the ENCODEprojectCAGE package, which is available for download from http://promshift.genereg.net/CAGEr/PackageSource/.")
#' }else if(!("package:ENCODEprojectCAGE" %in% search())){
#' stop("Requested CAGE data package is not loaded! Please load the data package by calling 'library(ENCODEprojectCAGE)'")
#' }
#'
#' if(dataset[1] == "ENCODEtissueCAGEfly"){
#' genome.name <- "BSgenome.Dmelanogaster.UCSC.dm3"
#' ENCODEtissueCAGEfly <- NULL
#' data("ENCODEtissueCAGEfly", envir = environment())
#' if(group == "embryo"){
#' if(sample == "mixed_embryos_0-24hr"){
#'
#' ctssTable <- ENCODEtissueCAGEfly[["embryo"]]
#'
#' }else{
#' stop("Specified sample not valid! The dataset 'ENCODEtissueCAGEfly' containes only one sample named 'mixed_embryos_0-24hr'!")
#' }
#' }else{
#' stop("Specified group not valid! The dataset 'ENCODEtissueCAGEfly' containes only one group named 'embryo'!")
#' }
#'
#' }else{
#' genome.name <- "BSgenome.Hsapiens.UCSC.hg19"
#' ENCODEhumanCellLinesSamples <- NULL
#' data("ENCODEhumanCellLinesSamples", envir = environment())
#' info.df <- ENCODEhumanCellLinesSamples
#'
#' if(!(all(dataset %in% info.df$dataset))){
#' stop("Specified dataset(s) not found! Call data(ENCODEhumanCellLinesSamples) and check 'dataset' column for available ENCODE datasets!")
#' }
#' if(length(dataset) == 1){
#'
#' if(!(all(group %in% info.df[info.df$dataset == dataset,"group"]))){
#' stop("Some of the provided groups cannot be found in the specified dataset!")
#' }
#' if(length(group) == 1){
#' if(!(all(sample %in% info.df[info.df$group == group,"sample"]))){
#' stop("Some of the provided samples cannot be found in the specified group!")
#' }
#' }else if(length(group) == length(sample)){
#' if(!all(sapply(c(1:length(group)), function(x) {sample[x] %in% info.df[info.df$group == group[x],"sample"]}))){
#' stop("Provided 'group' and 'sample' do not match! Some of the provided samples cannot be found in the corresponding groups!")
#' }
#' }else{
#' stop("Number of elements in the 'group' must be either 1 or must match the number of elements in the 'sample'!")
#' }
#'
#'
#' }else if(length(dataset) == length(group)){
#' if(!all(sapply(c(1:length(dataset)), function(x) {group[x] %in% info.df[info.df$dataset == dataset[x],"group"]}))){
#' stop("Provided 'dataset' and 'group' do not match! Some of the provided groups cannot be found in the corresponding datasets!")
#' }
#' if(length(group) == length(sample)){
#' if(!all(sapply(c(1:length(group)), function(x) {sample[x] %in% info.df[info.df$group == group[x],"sample"]}))){
#' stop("Provided 'group' and 'sample' do not match! Some of the provided samples cannot be found in the corresponding groups!")
#' }
#' }else{
#' stop("Number of elements in the 'group' must match the number of elements in the 'sample'!")
#' }
#'
#'
#' }else{
#' stop("Number of elements in the 'dataset' must be either 1 or must match the number of elements in the 'group' and in the 'sample'!")
#' }
#'
#' data(list = dataset, envir = environment())
#'
#' if(length(unique(dataset))>1){
#'
#' for(i in 1:length(unique(dataset))){
#' dset <- get(unique(dataset)[i])
#' for(j in 1:length(unique(group[which(dataset == unique(dataset)[i])]))){
#' g <- unique(group[which(dataset == unique(dataset)[i])])[j]
#' ctss <- dset[[g]][, c("chr", "pos", "strand", sample[which((group == g) & (dataset == unique(dataset)[i]))])]
#' discard <- apply(ctss[, c(4:ncol(ctss)), drop = F], 1, function(x) {sum(x > 0)}) >= 1
#' ctss <- data.table(ctss[discard,])
#' setkeyv(ctss, cols = c("chr", "pos", "strand"))
#' if(i == 1 & j == 1){
#' ctssTable <- ctss
#' }else{
#' ctssTable <- merge(ctssTable, ctss, all.x = T, all.y = T)
#' }
#' }
#' }
#' ctssTable[is.na(ctssTable)] <- 0
#'
#' }else{
#'
#' selected.dataset <- get(dataset)
#' if(length(unique(group))>1){
#'
#' for(i in 1:length(unique(group))){
#' g <- unique(group)[i]
#' ctss <- selected.dataset[[g]][, c("chr", "pos", "strand", sample[which(group == g)])]
#' discard <- apply(ctss[, c(4:ncol(ctss)), drop = F], 1, function(x) {sum(x > 0)}) >= 1
#' ctss <- data.table(ctss[discard,])
#' setkeyv(ctss, cols = c("chr", "pos", "strand"))
#' if(i == 1){
#' ctssTable <- ctss
#' }else{
#' ctssTable <- merge(ctssTable, ctss, all.x = T, all.y = T)
#' }
#' }
#'
#' ctssTable[is.na(ctssTable)] <- 0
#'
#' }else{
#' ctssTable <- selected.dataset[[group]][,c("chr", "pos", "strand", sample)]
#' }
#' }
#'
#' ctssTable <- data.frame(ctssTable, stringsAsFactors = F, check.names = F)
#' }
#'
#'
#' }else if(source == "FANTOM3and4"){
#'
#' if("FANTOM3and4CAGE" %in% rownames(installed.packages()) == FALSE){
#' stop("Requested CAGE data package is not installed! Please install and load the FANTOM3and4CAGE package available from Bioconductor.")
#' }else if(!("package:FANTOM3and4CAGE" %in% search())){
#' stop("Requested CAGE data package is not loaded! Please load the data package by calling 'library(FANTOM3and4CAGE)'")
#' }
#' FANTOMhumanSamples <- FANTOMmouseSamples <- NULL
#' data("FANTOMhumanSamples", envir = environment())
#' info.df1 <- FANTOMhumanSamples
#' data("FANTOMmouseSamples", envir = environment())
#' info.df2 <- FANTOMmouseSamples
#'
#' if(!(all(dataset %in% info.df1$dataset) | all(dataset %in% info.df2$dataset))){
#' stop("Specified dataset(s) not found! Call data(FANTOMhumanSamples) and data(FANTOMmouseSamples) and check 'dataset' column for available ENCODE datasets!")
#' }
#' if(length(grep("human", dataset))>0){
#' genome.name <- "BSgenome.Hsapiens.UCSC.hg18"
#' info.df <- info.df1
#' }else if(length(grep("mouse", dataset))>0){
#' genome.name <- "BSgenome.Mmusculus.UCSC.mm9"
#' info.df <- info.df2
#' }
#'
#' if(length(dataset) == 1){
#'
#' if(!(all(group %in% info.df[info.df$dataset == dataset,"group"]))){
#' stop("Some of the provided groups cannot be found in the specified dataset!")
#' }
#' if(length(group) == 1){
#' if(!(all(sample %in% info.df[info.df$group == group,"sample"]))){
#' stop("Some of the provided samples cannot be found in the specified group!")
#' }
#' }else if(length(group) == length(sample)){
#' if(!all(sapply(c(1:length(group)), function(x) {sample[x] %in% info.df[info.df$group == group[x],"sample"]}))){
#' stop("Provided 'group' and 'sample' do not match! Some of the provided samples cannot be found in the corresponding groups!")
#' }
#' }else{
#' stop("Number of elements in the 'group' must be either 1 or must match the number of elements in the 'sample'!")
#' }
#'
#'
#' }else if(length(dataset) == length(group)){
#' if(!all(sapply(c(1:length(dataset)), function(x) {group[x] %in% info.df[info.df$dataset == dataset[x],"group"]}))){
#' stop("Provided 'dataset' and 'group' do not match! Some of the provided groups cannot be found in the corresponding datasets!")
#' }
#' if(length(group) == length(sample)){
#' if(!all(sapply(c(1:length(group)), function(x) {sample[x] %in% info.df[info.df$group == group[x],"sample"]}))){
#' stop("Provided 'group' and 'sample' do not match! Some of the provided samples cannot be found in the corresponding groups!")
#' }
#' }else{
#' stop("Number of elements in the 'group' must match the number of elements in the 'sample'!")
#' }
#'
#'
#' }else{
#' stop("Number of elements in the 'dataset' must be either 1 or must match the number of elements in the 'group' and in the 'sample'!")
#' }
#'
#' data(list = dataset, envir = environment())
#'
#' if(length(unique(dataset))>1){
#'
#' for(i in 1:length(unique(dataset))){
#' dset <- get(unique(dataset)[i])
#' for(j in 1:length(unique(group[which(dataset == unique(dataset)[i])]))){
#' g <- unique(group[which(dataset == unique(dataset)[i])])[j]
#' ctss <- dset[[g]][, c("chr", "pos", "strand", sample[which(group == g)])]
#' discard <- apply(ctss[, c(4:ncol(ctss)), drop = F], 1, function(x) {sum(x > 0)}) >= 1
#' ctss <- data.table(ctss[discard,])
#' setnames(ctss, c("chr", "pos", "strand", paste(g, sample[which(group == g)], sep = "__")))
#' setkeyv(ctss, cols = c("chr", "pos", "strand"))
#' if(i == 1 & j == 1){
#' ctssTable <- ctss
#' }else{
#' ctssTable <- merge(ctssTable, ctss, all.x = T, all.y = T)
#' }
#' }
#' }
#'
#' ctssTable[is.na(ctssTable)] <- 0
#'
#' }else{
#'
#' selected.dataset <- get(dataset)
#' if(length(unique(group))>1){
#'
#' for(i in 1:length(unique(group))){
#' g <- unique(group)[i]
#' ctss <- selected.dataset[[g]][, c("chr", "pos", "strand", sample[which(group == g)])]
#' discard <- apply(ctss[, c(4:ncol(ctss)), drop = F], 1, function(x) {sum(x > 0)}) >= 1
#' ctss <- data.table(ctss[discard,])
#' setnames(ctss, c("chr", "pos", "strand", paste(g, sample[which(group == g)], sep = "__")))
#' setkeyv(ctss, cols = c("chr", "pos", "strand"))
#' if(i == 1){
#' ctssTable <- ctss
#' }else{
#' ctssTable <- merge(ctssTable, ctss, all.x = T, all.y = T)
#' }
#' }
#'
#' ctssTable[is.na(ctssTable)] <- 0
#'
#' }else{
#' ctssTable <- selected.dataset[[group]][,c("chr", "pos", "strand", sample)]
#' setnames(ctssTable, c("chr", "pos", "strand", paste(group, sample, sep = "__")))
#' }
#' }
#'
#' ctssTable <- data.frame(ctssTable, stringsAsFactors = F, check.names = F)
#'
#'
#'
#' }else if (source == "FANTOM5"){
#'
#' if(length(dataset) != 1){
#' stop("For FANTOM5 only one dataset can be specified and it can be either 'human' or 'mouse'!")
#' }else if(!(dataset %in% c("human", "mouse"))){
#' stop("For FANTOM5, dataset can be either 'human' or 'mouse'!")
#' }
#' if(dataset == "human"){
#' FANTOM5humanSamples <- NULL
#' data("FANTOM5humanSamples", envir = environment())
#' samples.info <- FANTOM5humanSamples
#' genome.name <- "BSgenome.Hsapiens.UCSC.hg19"
#' }else if(dataset == "mouse"){
#' FANTOM5mouseSamples <- NULL
#' data("FANTOM5mouseSamples", envir = environment())
#' samples.info <- FANTOM5mouseSamples
#' genome.name <- "BSgenome.Mmusculus.UCSC.mm9"
#' }
#'
#' if(!(all(sample %in% samples.info$sample))){
#' stop(paste("Some sample names cannot be found for the specified dataset! Call data(FANTOM5", dataset, "Samples) and check the 'sample' column for valid sample names!", sep = ""))
#' }
#'
#'
#'
#' for(i in c(1:length(sample))){
#'
#' message("Fetching sample: ", sample[i], "...")
#' sample.url <- samples.info[samples.info$sample == sample[i], "data_url"]
#' con <- gzcon(url(paste(sample.url)))
#' ctss <- scan(con, what = list(character(), NULL, integer(), NULL, integer(), character()))
#' ctss.df <- data.table(chr = ctss[[1]], pos = ctss[[3]], strand = ctss[[6]], tagCount = ctss[[5]])
#' setnames(ctss.df, c("chr", "pos", "strand", sample[i]))
#' setkeyv(ctss.df, cols = c("chr", "pos", "strand"))
#' if(i == 1){
#' ctss.table <- ctss.df
#' }else{
#' message("Adding sample to CTSS table...\n")
#' ctss.table <- merge(ctss.table, ctss.df, all.x = T, all.y = T)
#' ctss.table[is.na(ctss.table)] <- 0
#' }
#'
#' }
#'
#' ctssTable <- data.frame(ctss.table, stringsAsFactors = F, check.names = F)
#'
#'
#' }else if (source == "ZebrafishDevelopment"){
#'
#' if("ZebrafishDevelopmentalCAGE" %in% rownames(installed.packages()) == FALSE){
#' stop("Requested CAGE data package is not installed! Please install and load the ZebrafishDevelopmentalCAGE package, which is available for download from http://promshift.genereg.net/CAGEr/PackageSource/.")
#' }else if(!("package:ZebrafishDevelopmentalCAGE" %in% search())){
#' stop("Requested CAGE data package is not loaded! Please load the data package by calling 'library(ZebrafishDevelopmentalCAGE)'")
#' }
#'
#' ZebrafishSamples <- NULL
#' data("ZebrafishSamples", envir = environment())
#' if(dataset == "ZebrafishCAGE"){
#' if(group == "development"){
#' if(!(all(sample %in% ZebrafishSamples$sample))){
#' stop("Some sample names cannot be found for the specified dataset! Call data(ZebrafishSamples) and check the 'sample' column for valid sample names!")
#' }else{
#' genome.name <- "BSgenome.Drerio.UCSC.danRer7"
#' ZebrafishCAGE <- NULL
#' data("ZebrafishCAGE", envir = environment())
#' ctssTable <- ZebrafishCAGE[["development"]][,c("chr", "pos", "strand", sample)]
#' ctssTable <- ctssTable[apply(ctssTable[,4:ncol(ctssTable),drop=FALSE], 1, function(x) {any(x>0)}),]
#' }
#' }else{
#' stop("Invalid group name! There is only one group in this dataset named 'development'.")
#' }
#' }else{
#' stop("Invalid dataset name! There is only one available dataset named 'ZebrafishCAGE'.")
#' }
#'
#'
#' }else{
#' stop("Currently only the following public CAGE data resources are supported: 'FANTOM5', 'FANTOM3and4', 'ENCODE', 'ZebrafishDevelopment'. Refer to CAGEr vignette on how to use those resources!")
#' }
#'
#' rownames(ctssTable) <- c(1:nrow(ctssTable))
#'
#' sample.labels <- colnames(ctssTable)[4:ncol(ctssTable)]
#' names(sample.labels) <- rainbow(n = length(sample.labels))
#' myCAGEset <- new("CAGEset", genomeName = genome.name, inputFiles = paste(source, sample.labels, sep = "__"), inputFilesType = source, sampleLabels = sample.labels)
#' myCAGEset@librarySizes <- as.integer(colSums(ctssTable[,4:ncol(ctssTable),drop=FALSE]))
#' myCAGEset@CTSScoordinates <- ctssTable[, c("chr", "pos", "strand")]
#' myCAGEset@tagCountMatrix <- ctssTable[,4:ncol(ctssTable),drop=FALSE]
#'
#' return(myCAGEset)
#'
#' }
#' )
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.