GenotypeMatrix-class: Class 'GenotypeMatrix'
In podkat: Position-Dependent Kernel Association Test

Description Details Constructors Methods Accessors Subsetting Author(s) References See Also Examples

S4 class for storing genotypes efficiently as column-oriented sparse matrices along with variant info

This class stores genotypes as a column-oriented sparse numeric matrix, where rows correspond to samples and columns correspond to variants. This is accomplished by extending the dgCMatrix class from which this class inherits all slots. Information about variants is stored in an additional slot named variantInfo. This slot must be of class VariantInfo and have exactly as many elements as the genotype matrix has columns. The variantInfo slot has a dedicated metadata column named “MAF” that contains the minor allele frequencies (MAFs) of the variants. For convenience, accessor functions variantInfo and MAF are available (see below).

Objects of this class should only be created and manipulated by the constructors and accessors described below, as only these methods ensure the integrity of the created objects. Direct modification of object slots is strongly discouraged!

See help pages genotypeMatrix and readGenotypeMatrix.

show: signature(object="GenotypeMatrix"): displays the matrix dimensions (i.e. the number of samples and variants) along with some basic statistics of the minor allele frequency (MAF).

variantInfo: signature(object="GenotypeMatrix"): returns variant information as a VariantInfo object.
MAF: signature(object="GenotypeMatrix"): returns a numeric vector with the minor allele frequencies (MAFs).

Row and column names can be set and get as usual for matrix-like objects with rownames and colnames, respectively. When setting the column names of a GenotypeMatrix object, both the names of the variant info (slot variantInfo) and the column names of the matrix are set.

In the following code snippets, x is a GenotypeMatrix object.

: x[i, ]: returns a GenotypeMatrix object that only contains the samples selected by the index vector i
: x[, j]: returns a GenotypeMatrix object that only contains the variants selected by the index vector j
: x[i, j]: returns a GenotypeMatrix object that only contains the samples selected by the index vector i and the variants selected by the index vector j

None of these subsetting functions support a drop argument. As soon as a drop argument is supplied, no matter whether TRUE or FALSE, all variant information is stripped off and a dgCMatrix object is returned.

By default, MAFs are not altered by subsetting samples. However, if the optional argument recomputeMAF is set to TRUE (the default is FALSE), MAFs are recomputed for the resulting subsetted genotype matrix as described in genotypeMatrix. The ploidy for computing MAFs can be controlled by the optional ploidy argument (the default is 2).

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

http://www.bioinf.jku.at/software/podkat

dgCMatrix, VariantInfo, genotypeMatrix, readGenotypeMatrix

## create a toy example
A <- matrix(rbinom(50, 2, prob=0.2), 5, 10)
sA <- as(A, "dgCMatrix")
pos <- sort(sample(1:10000, ncol(A)))
seqname <- "chr1"

## variant with 'GRanges' object
gr <- GRanges(seqnames=seqname, ranges=IRanges(start=pos, width=1))
gtm <- genotypeMatrix(A, gr)
gtm
as.matrix(gtm)
variantInfo(gtm)
MAF(gtm)

## variant with 'pos' and 'seqnames' object
genotypeMatrix(sA, pos, seqname)

## variant with 'seqname:pos' strings passed through 'pos' argument
spos <- paste(seqname, pos, sep=":")
spos
genotypeMatrix(sA, spos)

## read data from VCF file using 'readVcf()' from the 'VariantAnnotation'
## package
if (require(VariantAnnotation))
{
    vcfFile <- system.file("examples/example1.vcf.gz", package="podkat")
    sp <- ScanVcfParam(info=NA, genome="GT", fixed=c("ALT", "FILTER"))
    vcf <- readVcf(vcfFile, genome="hgA", param=sp)
    rowRanges(vcf)

    ## call constructor for 'VCF' object
    gtm <- genotypeMatrix(vcf)
    gtm
    variantInfo(gtm)

    ## alternatively, extract information from 'VCF' object and use
    ## variant with character matrix and 'GRanges' positions
    ## note that, in 'VCF' objects, rows correspond to variants and
    ## columns correspond to samples, therefore, we have to transpose the
    ## genotype
    gt <- t(geno(vcf)$GT)
    gt[1:5, 1:5]
    gr <- rowRanges(vcf)
    gtm <- genotypeMatrix(gt, gr)
    as.matrix(gtm[1:20, 1:5, recomputeMAF=TRUE])
}