qCount(proj, query, reportLevel=c(NULL, "gene", "exon","promoter","junction"), selectReadPosition=c("start", "end"), shift=0L, orientation=c("any", "same", "opposite"), useRead=c("any", "first", "last"), auxiliaryName=NULL, mask=NULL, collapseBySample=TRUE, includeSpliced=TRUE, includeSecondary=TRUE, mapqMin=0L, mapqMax=255L, absIsizeMin=NULL, absIsizeMax=NULL, maxInsertSize=500L, clObj=NULL)qProject object representing a
sequencing experiment as returned by qAlignGRanges,
GRangesList or
TxDb with the regions to be
quantified. The type of query will determine the mode of
quantification (see ‘Details’). For
reportLevel="junction", query is ignored and can also
be NULL.query of type
TxDb or NULL), one of
gene (default): one value per gene
exon: one value per exon
promoter: one value per promoter
junction: one count per detected exon-exon junction
(query will be ignored in this case)
start (default): start of the alignment
end: end of the alignment
shift can be one of:
"halfInsert" (only available for
paired-end experiments)
The default of 0 will not shift any alignments.
any (default): count alignment on the same and opposite strand
same : count only alignment on the same strand
opposite : count only alignment on the opposite strand
any (default): count all alignments
first : count only alignments from the first read
last : count only alignments from the last read
NULL, a GRanges
object with reference regions to be masked, i.e. excluded from the
quantification, such as unmappable or highly repetitive regions (see
Details).TRUE (the default), sum alignment
counts from bam files with the same sample name.TRUE (the default), include spliced
alignments when counting. A spliced alignment is defined as an
alignment with a gap in the read of at least 60 bases.TRUE (the default), include alignments
with the secondary bit (0x0100) set in the FLAG when counting.mapqMin). Valid values are between 0 and 255. The default (0)
will include all alignments.mapqMax).
Valid values are between 0 and 255. The default (255) will include
all alignments.NULL (default),
which will not apply any minimum insert size filtering.NULL (default),
which will not apply any maximum insert size filtering.shift="halfInsert" and will
ensure that query regions are made wide enough to emcompass all
alignment pairs whose mid falls into the query region. The default
value is 500 bases.matrix with effective query regions width in the first
column, and alignment counts in subsequent columns, or a
GRanges object if reportLevel="junction".The effective query region width returned as first column in the
matrix is calculated by the number of unique, non-masked bases in the
reference sequence that contributed to the count of this query
name (irrespective if the bases were covered by alignments or not).
An effective width of zero indicates that the region was fully
masked and will have zero counts in all samples.The alignment counts in the matrix are contained from column two
onwards. For projects with allele-specific quantification, i.e. if a
file with single nucleotide polymorphisms was supplied to the
snpFile argument of qAlign, there will be
three columns per bam file (number of alignments for Reference,
Unknown and Alternative genotypes, with suffixed _R, _U and
_A). Otherwise there is a single columns per bam file.If collapseBySample=TRUE, groups of bam files with identical
sample name are combined by summing their alignment counts.For reportLevel="junction", the return value is a
GRanges object. The start and end coordinates correspond to the
first and last base in each detected intron. Plus- and minus-strand
alignments are quantified separately, so that in an unstranded RNA-seq
experiment, the same intron may be represented twice; once for each
strand. The counts for each sample are contained in the mcols
of the GRanges object.
qCount is used to count alignments in each sample from a
qProject object. The features to be quantified, together with
the mode of quantification, are specified by the query
argument, which is one of:
GRanges: Overlapping alignments
are counted separately for each coordinate region. If multiple
regions have identical names, their counts will be summed, counting
each alignment only once even if it overlaps more than one of these
regions. Alignments may be counted more than once if they overlap
multiple regions that have different names.
This mode is for example used to quantify ChIP-seq alignments in
promoter regions, or gene expression levels in an RNA-seq experiment
(using a query with exon regions named by gene).
GRangesList: Alignments are
counted and summed for each list element in query if they
overlap with any of the regions contained in the list element. The
order of the list elements defines a hierarchy for quantification:
Alignment will only be counted for the first element (the one with
the lowest index in query) that they overlap, but not for any
potential further list elements containing overlapping regions.
This mode can be used to hierarchically and uniquely count (assign)
each alignment to a one of several groups of regions (the elements
in query), for example to estimate the fractions of different
classes of RNA in an RNA-seq experiment (rRNA, tRNA, snRNA, snoRNA,
mRNA, etc.)
TxDb: Used to extract
regions from annotation and report alignment counts depending on the
value of reportLevel. If reportLevel="exon",
alignments overlapping each exon in query are counted.
If reportLevel="gene", alignment counts for all exons of a
gene will be summed, counting each alignment only once even if it
overlaps multiple annotated exons of a gene. These are useful to
calculate exon or gene expression levels in RNA-seq experiments
based on the annotation in a TxDb object. If
reportLevel="promoter", the promoters function from package
GenomicFeatures is used with default arguments to extract
promoter regions around transcript start sites, e.g. to quantify
alignments inf a ChIP-seq experiment.
NULL for
reportLevel="junction": The query argument is ignored
if reportLevel is set to "junction", and qCount
will count the number of alignments supporting each exon-exon
junction detected in any of the samples in proj. The
arguments selectReadPosition, shift,
orientation, useRead and mask will have no
effect in this quantification mode.
The additional arguments allow to fine-tune the quantification:
selectReadPosition defines the part of the alignment that has
to be contained within a query region for an overlap. The values
start (default) and end refer to the biological start
(5'-end) and end (3'-end) of the alignment. For example, the
start of an alignment on the plus strand is its leftmost
(lowest) base, and the end of an alignment on the minus strand
is also the leftmost base.
shift allows on-the-fly shifting of alignments towards their
3'-end prior to overlap determination and counting. This can be
helpful to increase resolution of ChIP-seq experiments by moving
alignments by half the immuno-precipitated fragment size towards the
middle of fragments. shift is either an “integer” vector
with one value per alignment file in proj, or a single
“integer” value, in which case all alignment files will be
shifted by the same value. For paired-end experiments, it can be
alternatively set to "halfInsert", which will estimate the true
fragment size from the distance between aligned read pairs and shift
the alignments accordingly.
orientation controls the interpretation of alignment strand
when counting, relative to the strand of the query region. any
will count all overlapping alignments, irrespective of the alignment
strand (e.g. used in an unstranded RNA-seq experiment). same
will only count the alignments on the same strand as the query region
(e.g. in a stranded RNA-seq experiment), and opposite will only
count the alignments on the opposite strand from the query region
(e.g. to quantify anti-sense transcription in a stranded RNA-seq
experiment).
includeSpliced and includeSecondary can be used to
include or exclude spliced or secondary alignments,
respectively. mapqMin and mapqMax allow to select alignments
based on their mapping qualities. mapqMin and mapqMax can
take integer values between 0 and 255 and equal to
$-10
log10 Pr(mapping position is wrong)$, rounded to the nearest
integer. A value 255 indicates that the mapping quality is not available.
In paired-end experiments, useRead allows to quantify either
all alignments (useRead="any"), or only the first
(useRead="first") or last (useRead="last") read from a
read pair or read group. Note that for useRead="any" (the
default), an alignment pair that is fully contained within a query
region will contribute two counts to the value of that
region. absIsizeMin and absIsizeMax can be used to
select alignments based on their insert size (TLEN field in SAM Spec
v1.4).
auxiliaryName selects the reference sequence for which
alignments should be quantified. NULL (the default) will
select alignments against the genome. If set to a character string
that matches one of the auxiliary target names (as specified in
the auxiliaryFile argument of qAlign), the
corresponding alignments will be counted.
mask can be used to specify a
GRanges object with regions in the
reference sequence to be excluded from quantification. The regions
will be considered unstranded (strand="*"). Alignments that
overlap with a region in mask will not be counted. Masking may
reduce the effective width of query regions reported by qCount,
even down to zero for regions that are fully contained in mask.
If clObj is set to an object that inherits from class
cluster, for example an object returned by
makeCluster from package parallel, the
quantification task is split into multiple chunks and processed in
parallel using clusterMap. Currently, not all
tasks will be efficiently parallelized: For example, a single query
region and a single (group of) bam files will not be split into
multiple chunks.
qAlign,
qProject,
makeCluster from package parallel
library(GenomicRanges)
library(Biostrings)
library(Rsamtools)
# copy example data to current working directory
file.copy(system.file(package="QuasR", "extdata"), ".", recursive=TRUE)
# load genome sequence
genomeFile <- "extdata/hg19sub.fa"
gseq <- readDNAStringSet(genomeFile)
chrRegions <- GRanges(names(gseq), IRanges(start=1,width=width(gseq),names=names(gseq)))
# create alignments (paired-end experiment)
sampleFile <- "extdata/samples_rna_paired.txt"
proj <- qAlign(sampleFile, genomeFile, splicedAlignment=TRUE)
# count reads using a "GRanges" query
qCount(proj, query=chrRegions)
qCount(proj, query=chrRegions, useRead="first")
# hierarchical counting using a "GRangesList" query
library(rtracklayer)
annotationFile <- "extdata/hg19sub_annotation.gtf"
gtfRegions <- import.gff(annotationFile, format="gtf", feature.type="exon")
names(gtfRegions) <- mcols(gtfRegions)$source
gtfRegionList <- split(gtfRegions, names(gtfRegions))
names(gtfRegionList)
res3 <- qCount(proj, gtfRegionList)
res3
# gene expression levels using a "TxDb" query
library("GenomicFeatures")
genomeRegion <- scanFaIndex(genomeFile)
chrominfo <- data.frame(chrom=as.character(seqnames(genomeRegion)),
length=end(genomeRegion),
is_circular=rep(FALSE, length(genomeRegion)))
txdb <- makeTxDbFromGFF(annotationFile,
format="gtf",
chrominfo=chrominfo,
dataSource="Ensembl modified",
organism="Homo sapiens")
res4 <- qCount(proj, txdb, reportLevel="gene")
res4
# exon-exon junctions
res5 <- qCount(proj, NULL, reportLevel="junction")
res5
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