normCounts: Normalise a counts matrix to the median library size

In Oshlack/speckle: Statistical methods for analysing single cell RNA-seq data

Normalise a counts matrix to the median library size

Description

This function takes a DGEList object or matrix of counts and normalises the counts to the median library size. This puts the normalised counts on a similar scale to the original counts.

Usage

normCounts(x, log = FALSE, prior.count = 0.5, lib.size = NULL)

Arguments

x	a DGEList object or matrix of counts.
log	logical, indicates whether the output should be on the log2 scale or counts scale. Default is FALSE.
prior.count	The prior count to add if the data is log2 normalised. Default is a small count of 0.5.
lib.size	a vector of library sizes to be used during the normalisation step. Default is NULL and will be computed from the counts matrix.

Details

If the input is a DGEList object, the normalisation factors in norm.factors are taken into account in the normalisation. The prior counts are added proportionally to the library size

Value

a matrix of normalised counts

Author(s)

Belinda Phipson

Examples

# Simulate some data from a negative binomial distribution with mean equal
# to 100 and dispersion set to 1. Simulate 1000 genes and 6 samples.
y <- matrix(rnbinom(6000, mu = 100, size = 1), ncol = 6)

# Normalise the counts
norm.y <- normCounts(y)

# Return log2 normalised counts
lnorm.y <- normCounts(y, log=TRUE)

# Return log2 normalised counts with prior.count = 2
lnorm.y2 <- normCounts(y, log=TRUE, prior.count=2)

par(mfrow=c(1,2))
boxplot(norm.y, main="Normalised counts")
boxplot(lnorm.y, main="Log2-normalised counts")

Oshlack/speckle documentation built on Oct. 16, 2022, 9:39 a.m.