summarizeOverlaps-methods {GenomicAlignments} | R Documentation

## Perform overlap queries between reads and genomic features

### Description

`summarizeOverlaps` extends `findOverlaps` by providing options to resolve reads that overlap multiple features.

### Usage

    ## S4 method for signature 'GRanges,GAlignments'
    summarizeOverlaps(
      features, reads, mode=Union,
      ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...)

    ## S4 method for signature 'GRangesList,GAlignments'
    summarizeOverlaps(
      features, reads, mode=Union,
      ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...)

    ## S4 method for signature 'GRanges,GRanges'
    summarizeOverlaps(
      features, reads, mode=Union,
      ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...)

    ## S4 method for signature 'GRangesList,GRanges'
    summarizeOverlaps(
      features, reads, mode=Union,
      ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...)

    ## S4 method for signature 'GRanges,GAlignmentPairs'
    summarizeOverlaps(
      features, reads, mode=Union,
      ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...)

    ## S4 method for signature 'GRangesList,GAlignmentPairs'
    summarizeOverlaps(
      features, reads, mode=Union,
      ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...)

    ## mode funtions
    Union(features, reads, ignore.strand=FALSE,
                           inter.feature=TRUE)
    IntersectionStrict(features, reads, ignore.strand=FALSE,
                                        inter.feature=TRUE)
    IntersectionNotEmpty(features, reads, ignore.strand=FALSE,
                                          inter.feature=TRUE)

    ## S4 method for signature 'GRanges,BamFile'
    summarizeOverlaps(
      features, reads, mode=Union,
      ignore.strand=FALSE, inter.feature=TRUE, singleEnd=TRUE,
      fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...)

    ## S4 method for signature 'BamViews,missing'
    summarizeOverlaps(
      features, reads, mode=Union,
      ignore.strand=FALSE, inter.feature=TRUE, singleEnd=TRUE,
      fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...)

### Arguments

`features` | A GRanges or a GRangesList object of genomic regions of interest. When a GRanges is supplied, each row is considered a feature. When a GRangesList is supplied, each higher list-level is considered a feature. This distinction is important when defining overlaps. When `features` is a BamViews the `reads` argument is missing. Features are extracted from the `bamRanges` and the `reads` from `bamPaths`. Metadata from `bamPaths` and `bamSamples` are stored in the `colData` of the resulting RangedSummarizedExperiment object. `bamExperiment` metadata are stored in the `metadata` slot.
`reads` | A GRanges, GRangesList GAlignments, GAlignmentsList, GAlignmentPairs, BamViews or BamFileList object that represents the data to be counted by `summarizeOverlaps`. `reads` is missing when a BamViews object is the only argument supplied to `summarizeOverlaps`. `reads` are the files specified in `bamPaths` of the BamViews object.
`mode` | `mode` can be one of the pre-defined count methods such as "Union", "IntersectionStrict", or "IntersectionNotEmpty" or it a user supplied count function. For a custom count function, the input arguments must match those of the pre-defined options and the function must return a vector of counts the same length as the annotation ('features' argument). See examples for details. The pre-defined options are designed after the counting modes available in the HTSeq package by Simon Anders (see references). "Union" : (Default) Reads that overlap any portion of exactly one feature are counted. Reads that overlap multiple features are discarded. This is the most conservative of the 3 modes. "IntersectionStrict" : A read must fall completely "within" the feature to be counted. If a read overlaps multiple features but falls "within" only one, the read is counted for that feature. If the read is "within" multiple features, the read is discarded. "IntersectionNotEmpty" : A read must fall in a unique disjoint region of a feature to be counted. When a read overlaps multiple features, the features are partitioned into disjoint intervals. Regions that are shared between the features are discarded leaving only the unique disjoint regions. If the read overlaps one of these remaining regions, it is assigned to the feature the unique disjoint region came from. user supplied function : A function can be supplied as the `mode` argument. It must (1) have arguments that correspond to `features`, `reads`, `ignore.strand` and `inter.feature` arguments (as in the defined mode functions) and (2) return a vector of counts the same length as `features`.
`ignore.strand` | A logical indicating if strand should be considered when matching.
`inter.feature` | (Default TRUE) A logical indicating if the counting `mode` should be aware of overlapping features. When TRUE (default), reads mapping to multiple features are dropped (i.e., not counted). When FALSE, these reads are retained and a count is assigned to each feature they map to. There are 6 possible combinations of the `mode` and `inter.feature` arguments. When `inter.feature=FALSE` the behavior of modes ‘Union’ and ‘IntersectionStrict’ are essentially ‘countOverlaps’ with ‘type=any’ and `type=within`, respectively. ‘IntersectionNotEmpty’ does not reduce to a simple countOverlaps because common (shared) regions of the annotation are removed before counting.
`preprocess.reads` | A function applied to the reads before counting. The first argument should be `reads` and the return value should be an object compatible with the `reads` argument to the counting modes, Union, IntersectionStrict and IntersectionNotEmpty. The distinction between a user-defined 'mode' and user-defined 'preprocess.reads' function is that in the first case the user defines how to count; in the second case the reads are preprocessed before counting with a pre-defined mode. See examples.
`...` | Additional arguments passed to functions or methods called from within `summarizeOverlaps`. For BAM file methods arguments may include `singleEnd`, `fragments` or `param` which apply to reading records from a file (see below). Providing `count.mapped.reads=TRUE` include additional passes through the BAM file to collect statistics similar to those from `countBam`. A `BPPARAM` argument can be passed down to the `bplapply` called by `summarizeOverlaps`. The argument can be MulticoreParam(), SnowParam(), BatchJobsParam() or DoparParam(). See the BiocParallel package for details in specifying the params.
`singleEnd` | (Default TRUE) A logical indicating if reads are single or paired-end. In Bioconductor > 2.12 it is not necessary to sort paired-end BAM files by `qname`. When counting with `summarizeOverlaps`, setting `singleEnd=FALSE` will trigger paired-end reading and counting. It is fine to also set `asMates=TRUE` in the `BamFile` but is not necessary when `singleEnd=FALSE`.
`fragments` | (Default FALSE) A logical; applied to paired-end data only. `fragments` controls which function is used to read the data which subsequently affects which records are included in counting. When `fragments=FALSE`, data are read with `readGAlignmentPairs` and returned in a `GAlignmentPairs` class. In this case, singletons, reads with unmapped pairs, and other fragments, are dropped. When `fragments=TRUE`, data are read with `readGAlignmentsList` and returned in a `GAlignmentsList` class. This class holds ‘mated pairs’ as well as same-strand pairs, singletons, reads with unmapped pairs and other fragments. Because more records are kept, generally counts will be higher when `fragments=TRUE`. The term ‘mated pairs’ refers to records paired with the algorithm described on the `?readGAlignmentsList` man page.
`param` | An optional `ScanBamParam` instance to further influence scanning, counting, or filtering. See `?BamFile` for details of how records are returned when both `yieldSize` is specified in a `BamFile` and `which` is defined in a `ScanBamParam`.

### Details

  `summarizeOverlaps` offers counting modes to resolve reads that overlap multiple features. The `mode` argument defines a set of rules to resolve the read to a single feature such that each read is counted a maximum of once. New to GenomicRanges >= 1.13.9 is the `inter.feature` argument which allows reads to be counted for each feature they overlap. When `inter.feature=TRUE` the counting modes are aware of feature overlap; reads that overlap multiple features are dropped and not counted. When `inter.feature=FALSE` multiple feature overlap is ignored and reads are counted once for each feature they map to. This essentially reduces modes ‘Union’ and ‘IntersectionStrict’ to `countOverlaps` with `type="any"`, and `type="within"`, respectively. ‘IntersectionNotEmpty’ is not reduced to a derivative of `countOverlaps` because the shared regions are removed before counting.

  The `BamViews`, `BamFile` and `BamFileList` methods summarize overlaps across one or several files. The latter uses `bplapply`; control parallel evaluation using the `register` interface in the BiocParallel package.

features :

  A ‘feature’ can be any portion of a genomic region such as a gene, transcript, exon etc. When the `features` argument is a GRanges the rows define the features. The result will be the same length as the GRanges. When `features` is a GRangesList the highest list-level defines the features and the result will be the same length as the GRangesList.

  When `inter.feature=TRUE`, each count `mode` attempts to assign a read that overlaps multiple features to a single feature. If there are ranges that should be considered together (e.g., exons by transcript or cds regions by gene) the GRangesList would be appropriate. If there is no grouping in the data then a GRanges would be appropriate.

paired-end reads :

  Paired-end reads are counted as a single hit if one or both parts of the pair are overlapped. Paired-end records can be counted in a GAlignmentPairs container or BAM file.

  Counting pairs in BAM files:

  - The `singleEnd` argument should be FALSE.

  - When `reads` are supplied as a BamFile or BamFileList, the `asMates` argument to the BamFile should be TRUE.

  - When `fragments` is FALSE, a `GAlignmentPairs` object is used in counting (pairs only).

  - When `fragments` is TRUE, a `GAlignmentsList` object is used in counting (pairs, singletons, unmapped mates, etc.)

### Value

A RangedSummarizedExperiment object. The `assays` slot holds the counts, `rowRanges` holds the annotation from `features`.

When `reads` is a `BamFile` or `BamFileList` `colData` is an empty DataFrame with a single row named ‘counts’. If `count.mapped.reads=TRUE`, `colData` holds the output of `countBam` in 3 columns named ‘records’ (total records), ‘nucleotides’ and ‘mapped’ (mapped records).

When `features` is a `BamViews` `colData` includes 2 columns named `bamSamples` and `bamIndices`.

In all other cases, `colData` has columns of ‘object’ (class of reads) and ‘records’ (length of `reads`).

### Author(s)

Valerie Obenchain <Valerie.Obenchain@RoswellPark.org>

### References

HTSeq : http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html

htseq-count : http://www-huber.embl.de/users/anders/HTSeq/doc/count.html

### See Also

- The DESeq2, DEXSeq and edgeR packages.

- The RangedSummarizedExperiment class defined in the SummarizedExperiment package.

- The GAlignments and GAlignmentPairs classes.

- The BamFileList and BamViews classes in the Rsamtools package.

- The readGAlignments and readGAlignmentPairs functions.

### Examples

    reads <- GAlignments(
        names = c("a","b","c","d","e","f","g"),
        seqnames = Rle(c(rep(c("chr1", "chr2"), 3), "chr1")),
        pos = as.integer(c(1400, 2700, 3400, 7100, 4000, 3100, 5200)),
        cigar = c("500M", "100M", "300M", "500M", "300M",
                  "50M200N50M", "50M150N50M"),
        strand = strand(rep("+", 7)))

    gr <- GRanges(
        seqnames = c(rep("chr1", 7), rep("chr2", 4)), strand = "+",
        ranges = IRanges(c(1000, 3000, 3600, 4000, 4000, 5000, 5400,
                           2000, 3000, 7000, 7500),
                         width = c(500, 500, 300, 500, 900, 500, 500,
                                   900, 500, 600, 300),
                         names=c("A", "B", "C1", "C2", "D1", "D2", "E", "F",
                                 "G", "H1", "H2")))
    groups <- factor(c(1,2,3,3,4,4,5,6,7,8,8))
    grl <- splitAsList(gr, groups)
    names(grl) <- LETTERS[seq_along(grl)]

    ## ---------------------------------------------------------------------
    ## Counting modes.
    ## ---------------------------------------------------------------------

    ## First count with a GRanges as the 'features'. 'Union' is the
    ## most conservative counting mode followed by 'IntersectionStrict'
    ## then 'IntersectionNotEmpty'.
    counts1 <-
        data.frame(union=assays(summarizeOverlaps(gr, reads))$counts,
                   intStrict=assays(summarizeOverlaps(gr, reads,
                                    mode="IntersectionStrict"))$counts,
                   intNotEmpty=assays(summarizeOverlaps(gr, reads,
                                      mode="IntersectionNotEmpty"))$counts)

    colSums(counts1)

    ## Split the 'features' into a GRangesList and count again.
    counts2 <-
        data.frame(union=assays(summarizeOverlaps(grl, reads))$counts,
                   intStrict=assays(summarizeOverlaps(grl, reads,
                                    mode="IntersectionStrict"))$counts,
                   intNotEmpty=assays(summarizeOverlaps(grl, reads,
                                      mode="IntersectionNotEmpty"))$counts)
    colSums(counts2)

    ## The GRangesList ('grl' object) has 8 features whereas the GRanges
    ## ('gr' object) has 11. The affect on counting can be seen by looking
    ## at feature 'H' with mode 'Union'. In the GRanges this feature is
    ## represented by ranges 'H1' and 'H2',
    gr[c("H1", "H2")]

    ## and by list element 'H' in the GRangesList,
    grl["H"]

    ## Read "d" hits both 'H1' and 'H2'. This is considered a multi-hit when
    ## using a GRanges (each range is a separate feature) so the read was
    ## dropped and not counted.
    counts1[c("H1", "H2"), ]

    ## When using a GRangesList, each list element is considered a feature.
    ## The read hits multiple ranges within list element 'H' but only one
    ## list element. This is not considered a multi-hit so the read is counted.
    counts2["H", ]

    ## ---------------------------------------------------------------------
    ## Counting multi-hit reads.
    ## ---------------------------------------------------------------------

    ## The goal of the counting modes is to provide a set of rules that
    ## resolve reads hitting multiple features so each read is counted
    ## a maximum of once. However, sometimes it may be desirable to count
    ## a read for each feature it overlaps. This can be accomplished by
    ## setting 'inter.feature' to FALSE.

    ## When 'inter.feature=FALSE', modes 'Union' and 'IntersectionStrict'
    ## essentially reduce to countOverlaps() with type="any" and
    ## type="within", respectively.

    ## When 'inter.feature=TRUE' only features "A", "F" and "G" have counts.
    se1 <- summarizeOverlaps(gr, reads, mode="Union", inter.feature=TRUE)
    assays(se1)$counts

    ## When 'inter.feature=FALSE' all 11 features have a count. There are
    ## 7 total reads so one or more reads were counted more than once.
    se2 <- summarizeOverlaps(gr, reads, mode="Union", inter.feature=FALSE)
    assays(se2)$counts

    ## ---------------------------------------------------------------------
    ## Counting BAM files.
    ## ---------------------------------------------------------------------

    library(pasillaBamSubset)
    library(TxDb.Dmelanogaster.UCSC.dm3.ensGene)
    exbygene <- exonsBy(TxDb.Dmelanogaster.UCSC.dm3.ensGene, "gene")

    ## (i) Single-end :

    ## Large files can be iterated over in chunks by setting a
    ## 'yieldSize' on the BamFile.
    bf_s <- BamFile(untreated1_chr4(), yieldSize=50000)
    se_s <- summarizeOverlaps(exbygene, bf_s, singleEnd=TRUE)
    table(assays(se_s)$counts > 0)

    ## When a character (file name) is provided as 'reads' instead
    ## of a BamFile object summarizeOverlaps() will create a BamFile
    ## and set a reasonable default 'yieldSize'.

    ## (ii) Paired-end :

    ## A paired-end file may contain singletons, reads with unmapped
    ## pairs or reads with more than two fragments. When 'fragments=FALSE'
    ## only reads paired by the algorithm are included in the counting.
    nofrag <- summarizeOverlaps(exbygene, untreated3_chr4(),
                                singleEnd=FALSE, fragments=FALSE)
    table(assays(nofrag)$counts > 0)

    ## When 'fragments=TRUE' all singletons, reads with unmapped pairs
    ## and other fragments will be included in the counting.
    bf <- BamFile(untreated3_chr4(), asMates=TRUE)
    frag <- summarizeOverlaps(exbygene, bf, singleEnd=FALSE, fragments=TRUE)
    table(assays(frag)$counts > 0)

    ## As expected, using 'fragments=TRUE' results in a larger number
    ## of total counts because singletons, unmapped pairs etc. are
    ## included in the counting.

    ## Total reads in the file:
    countBam(untreated3_chr4())

    ## Reads counted with 'fragments=FALSE':
    sum(assays(nofrag)$counts)

    ## Reads counted with 'fragments=TRUE':
    sum(assays(frag)$counts)

    ## ---------------------------------------------------------------------
    ## Use ouput of summarizeOverlaps() for differential expression analysis
    ## with DESeq2 or edgeR.
    ## ---------------------------------------------------------------------

    fls <- list.files(system.file("extdata", package="GenomicAlignments"),
                      recursive=TRUE, pattern="*bam$", full=TRUE)
    names(fls) <- basename(fls)
    bf <- BamFileList(fls, index=character(), yieldSize=1000)
    genes <- GRanges(
        seqnames = c(rep("chr2L", 4), rep("chr2R", 5), rep("chr3L", 2)),
        ranges = IRanges(c(1000, 3000, 4000, 7000, 2000, 3000, 3600,
                           4000, 7500, 5000, 5400),
                         width=c(rep(500, 3), 600, 900, 500, 300, 900,
                                 300, 500, 500)))
    se <- summarizeOverlaps(genes, bf)

    ## When the reads are BAM files, the 'colData' contains summary
    ## information from a call to countBam().
    colData(se)

    ## Start differential expression analysis with the DESeq2 or edgeR
    ## package:
    library(DESeq2)
    deseq <- DESeqDataSet(se, design= ~ 1)
    library(edgeR)
    edger <- DGEList(assays(se)$counts, group=rownames(colData(se)))

    ## ---------------------------------------------------------------------
    ## Filter records by map quality before counting.
    ## (user-supplied 'mode' function)
    ## ---------------------------------------------------------------------

    ## The 'mode' argument can take a custom count function whose
    ## arguments are the same as those in the current counting modes
    ## (i.e., Union, IntersectionNotEmpty, IntersectionStrict).
    ## In this example records are filtered by map quality before counting.

    mapq_filter <- function(features, reads, ignore.strand, inter.feature)
    {
        require(GenomicAlignments) # needed for parallel evaluation
        Union(features, reads[mcols(reads)$mapq >= 20],
              ignore.strand, inter.feature)
    }

    genes <- GRanges("seq1", IRanges(seq(1, 1500, by=200), width=100))
    param <- ScanBamParam(what="mapq")
    fl <- system.file("extdata", "ex1.bam", package="Rsamtools")
    se <- summarizeOverlaps(genes, fl, mode=mapq_filter, param=param)
    assays(se)$counts

    ## The count function can be completely custom (i.e., not use the
    ## pre-defined count functions at all). Requirements are that
    ## the input arguments match the pre-defined modes and the output
    ## is a vector of counts the same length as 'features'.

    my_count <- function(features, reads,  ignore.strand, inter.feature) {
        ## perform filtering, or subsetting etc.
        require(GenomicAlignments) # needed for parallel evaluation
        countOverlaps(features, reads)
    }

    ## ---------------------------------------------------------------------
    ## Preprocessing reads before counting with a standard count mode.
    ## (user-supplied 'preprocess.reads' function)
    ## ---------------------------------------------------------------------

    ## The 'preprocess.reads' argument takes a function that is
    ## applied to the reads before counting with a pre-defined mode.

    ResizeReads <- function(reads, width=1, fix="start", ...) {
        reads <- as(reads, "GRanges")
        stopifnot(all(strand(reads) != "*"))
        resize(reads, width=width, fix=fix, ...)
    }

    ## By default ResizeReads() counts reads that overlap on the 5' end:
    summarizeOverlaps(grl, reads, mode=Union, preprocess.reads=ResizeReads)

    ## Count reads that overlap on the 3' end by passing new values
    ## for 'width' and 'fix':
    summarizeOverlaps(grl, reads, mode=Union, preprocess.reads=ResizeReads,
                      width=1, fix="end")

    ## ---------------------------------------------------------------------
    ## summarizeOverlaps() with BamViews.
    ## ---------------------------------------------------------------------

    ## bamSamples and bamPaths metadata are included in the colData.
    ## bamExperiment metadata is put into the metadata slot.
    fl <- system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE)
    rngs <- GRanges(c("seq1", "seq2"), IRanges(1, c(1575, 1584)))
    samp <- DataFrame(info="test", row.names="ex1")
    view <- BamViews(fl, bamSamples=samp, bamRanges=rngs)
    se <- summarizeOverlaps(view, mode=Union, ignore.strand=TRUE)
    colData(se)
    metadata(se)

[Package GenomicAlignments version 1.24.0 Index]