fpkm {DESeq2} | R Documentation

## FPKM: fragments per kilobase per million mapped fragments

### 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

The length of the features (e.g. genes) is calculated one of two ways: (1) If there is a matrix named "avgTxLength" in `assays(dds)`, this will take precedence in the length normalization. This occurs when using the tximport-DESeq2 pipeline. (2) Otherwise, feature length is calculated from the `rowRanges` of the dds object, if a column `basepairs` is not present in `mcols(dds)`. The calculated length 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. Note that the second approach over-estimates the gene length (average transcript length, weighted by abundance is a more appropriate normalization for gene counts), and so the FPKM will be an underestimate of the true value.

Note that, when the read/fragment counting has inter-feature dependencies, a strict normalization would not incorporate the basepairs of a feature which overlap another feature. This inter-feature 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(list(counts=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)) # 1kb
    gr2 <- GRanges("chr1",IRanges(c(1,1001),c( 500,1500))) # 1kb
    gr3 <- GRanges("chr1",IRanges(c(1,1001),c(1000,2000))) # 2kb
    gr4 <- GRanges("chr1",IRanges(c(1,1001),c(200,1300))) # 500bp
    rowRanges(dds) <- GRangesList(gr1,gr2,gr3,gr4)

    # the raw counts
    counts(dds)

    # the FPM values
    fpm(dds)

    # the FPKM values
    fpkm(dds)

[Package DESeq2 version 1.32.0 Index]