fpm {DESeq2} | R Documentation

## FPM: fragments per million mapped fragments

### Description

Calculates either a robust version (default) or the traditional matrix of fragments/counts per million mapped fragments (FPM/CPM). Note: this function is written very simply and can be easily altered to produce other behavior by examining the source code.

### Usage

    fpm(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. If TRUE, the size factors and the geometric mean of column sums are multiplied to create a robust library size estimate. Robust normalization is not used if average transcript lengths are present.

### Value

a matrix which is normalized per million of mapped fragments, either using the robust median ratio method (robust=TRUE, default) or using raw counts (robust=FALSE).

### See Also

`fpkm`

### Examples

    # generate a dataset with size factors: .5, 1, 1, 2
    dds <- makeExampleDESeqDataSet(m = 4, n = 1000,
                                   interceptMean=log2(1e3),
                                   interceptSD=0,
                                   sizeFactors=c(.5,1,1,2),
                                   dispMeanRel=function(x) .01)

    # add a few rows with very high count
    counts(dds)[4:10,] <- 2e5L

    # in this robust version, the counts are comparable across samples
    round(head(fpm(dds), 3))

    # in this column sum version, the counts are still skewed:
    # sample1 < sample2 & 3 < sample 4
    round(head(fpm(dds, robust=FALSE), 3))

    # the column sums of the robust version
    # are not equal to 1e6, but the
    # column sums of the non-robust version
    # are equal to 1e6 by definition

    colSums(fpm(dds))/1e6
    colSums(fpm(dds, robust=FALSE))/1e6

[Package DESeq2 version 1.32.0 Index]