fpkm: FPKM: fragments per kilobase per million mapped fragments

In aghozlane/DESeq2shaman: Differential gene expression analysis based on the negative binomial distribution

Description

The following function returns fragment counts normalized per kilobase of feature length per million mapped fragments (by default using a robust estimate of the library size, as in estimateSizeFactors).

Usage

fpkm(object, robust = TRUE)

Arguments

object	a DESeqDataSet
robust	whether to use size factors to normalize rather than taking the column sums of the raw counts, using the fpm function.

Details

Note: the kilobase length of the features is calculated from the rowData if a column basepairs is not present in mcols(dds). This is the number of basepairs in the union of all GRanges assigned to a given row of object, e.g., the union of all basepairs of exons of a given gene. When the read/fragment counting is interfeature dependent, a strict normalization would not incorporate the basepairs of a feature which overlap another feature. This interfeature dependence is not taken into consideration in the internal union basepair calculation.

Value

a matrix which is normalized per kilobase of the union of basepairs in the GRangesList or GRanges of the mcols(object), and per million of mapped fragments, either using the robust median ratio method (robust=TRUE, default) or using raw counts (robust=FALSE). Defining a column mcols(object)$basepairs takes precedence over internal calculation of the kilobases for each row.

See Also

fpm

Examples

# create a matrix with 1 million counts for the
# 2nd and 3rd column, the 1st and 4th have
# half and double the counts, respectively.
m <- matrix(1e6 * rep(c(.125, .25, .25, .5), each=4),
            ncol=4, dimnames=list(1:4,1:4))
mode(m) <- "integer"
se <- SummarizedExperiment(m, colData=DataFrame(sample=1:4))
dds <- DESeqDataSet(se, ~ 1)

# create 4 GRanges with lengths: 1, 1, 2, 2.5 Kb
gr1 <- GRanges("chr1",IRanges(1,1000))
gr2 <- GRanges("chr1",IRanges(c(1,501),c(500,1000)))
gr3 <- GRanges("chr1",IRanges(c(1,1001),c(1000,2000)))
gr4 <- GRanges("chr1",IRanges(c(1,1001,2001),c(500,3000,3000)))
rowData(dds) <- GRangesList(gr1,gr2,gr3,gr4)

# the raw counts
counts(dds)

# the FPKM values
fpkm(dds)

# held constant per 1 million fragments
counts(dds) <- counts(dds) * 2L
round(fpkm(dds))

aghozlane/DESeq2shaman documentation built on Nov. 1, 2019, 9:01 p.m.