Augment observed read counts with prior count

Augment observed read counts with prior count since log of zero counts is undefined. The prior count added to each sample is scaled so that no variance is introduced

Usage

augmentPriorCount(
  counts,
  lib.size = colSums2(counts),
  prior.count = 0.5,
  scaledByLib = FALSE
)

Arguments

counts

matrix of read counts with genes as rows and samples as columns

lib.size

library sizes, the sum of all ready for each sample

prior.count

average prior count added to each sample.

scaledByLib

if TRUE, scale pseudocount by lib.size. Else to standard constant pseudocount addition

Value

matrix with augmented counts

Details

Adding prior counts removes the issue of evaluating the log of zero counts, and stabilizes the log transform when counts are very small. However, adding a constant prior count to all samples can introduced an artifact. Consider two samples each with zero counts for a given gene, but one as a library size of 1k and the other of 50k. After applying the prior count values become pc / 1k and pc / 50k. It appears that there is variance in the expression of this gene, even though no counts are observed. This is driven only by variation in the library size, which does not reflect biology. This issue is most problematic for small counts.

Instead, we make the reasonable assumption that a gene does not have expression variance unless supported sufficiently by counts in the numerator. Consider adding a different prior count to each sample so that genes with zero counts end up woth zero variance. This corresponds to adding prior.count * lib.size[i] / mean(lib.size) to sample i.

This is done in the backend of edgeR::cpm(), but this function allows users to apply it more generally.

See also

edgeR::cpm()

Examples

library(edgeR)

data(varPartDEdata)

# normalize RNA-seq counts
dge <- DGEList(counts = countMatrix)
dge <- calcNormFactors(dge)

countsAugmented <- augmentPriorCount( dge$counts, dge$samples$lib.size, 1)