qCount {QuasR} | R Documentation

## Quantify alignments

### Description

Quantify alignments from sequencing data.

### Usage

    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
    )

### Arguments

`proj` | A `qProject` object representing a sequencing experiment as returned by `qAlign`
`query` | An object of type `GRanges`, `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`.
`reportLevel` | Level of quantification (`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
`selectReadPosition` | defines the part of the alignment that has to be contained within a query region to produce an overlap (see ‘Details’). Possible values are: `start` (default): start of the alignment `end`: end of the alignment
`shift` | controls the shifting alignments towards their 3'-end before quantification. `shift` can be one of: an “integer” vector of the same length as the number of alignment files a single “integer” value the character string `"halfInsert"` (only available for paired-end experiments) The default of `0` will not shift any alignments.
`orientation` | sets the required orientation of the alignments relative to the query region in order to be counted, one of: `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
`useRead` | For paired-end experiments, selects the read mate whose alignments should be counted, one of: `any` (default): count all alignments `first` : count only alignments from the first read `last` : count only alignments from the last read
`auxiliaryName` | Which bam files to use in an experiments with auxiliary alignments (see ‘Details’).
`mask` | If not `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’).
`collapseBySample` | If `TRUE` (the default), sum alignment counts from bam files with the same sample name.
`includeSpliced` | If `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.
`includeSecondary` | If `TRUE` (the default), include alignments with the secondary bit (0x0100) set in the `FLAG` when counting.
`mapqMin` | Minimal mapping quality of alignments to be included when counting (mapping quality must be greater than or equal to `mapqMin`). Valid values are between 0 and 255. The default (0) will include all alignments.
`mapqMax` | Maximal mapping quality of alignments to be included when counting (mapping quality must be less than or equal to `mapqMax`). Valid values are between 0 and 255. The default (255) will include all alignments.
`absIsizeMin` | For paired-end experiments, minimal absolute insert size (TLEN field in SAM Spec v1.4) of alignments to be included when counting. Valid values are greater than 0 or `NULL` (default), which will not apply any minimum insert size filtering.
`absIsizeMax` | For paired-end experiments, maximal absolute insert size (TLEN field in SAM Spec v1.4) of alignments to be included when counting. Valid values are greater than 0 or `NULL` (default), which will not apply any maximum insert size filtering.
`maxInsertSize` | Maximal fragment size of the paired-end experiment. This parameter is used if `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.
`clObj` | A cluster object to be used for parallel processing (see ‘Details’).

### Details

`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.

- any of the above or `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 that generates reads from the sense strand), 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.

### Value

A `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.

### Author(s)

Anita Lerch, Dimos Gaidatzis and Michael Stadler

### See Also

`qAlign`, `qProject`, `makeCluster` from package parallel

### Examples

    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

[Package QuasR version 1.32.0 Index]