edge (version 2.4.2)

equalizeLibSizes: Equalize Library Sizes by Quantile-to-Quantile Normalization

Description

Adjusts counts so that the effective library sizes are equal, preserving fold-changes between groups and preserving biological variability within each group.

Usage

## S3 method for class 'DGEList':
equalizeLibSizes(y, dispersion=NULL, \dots)
## S3 method for class 'default':
equalizeLibSizes(y, group=NULL, dispersion=NULL, 
            lib.size=NULL, \dots)

Arguments

y

matrix of counts or a DGEList object.

dispersion

numeric scalar or vector of dispersion parameters. By default, is extracted from y or, if y contains no dispersion information, is set to 0.05.

group

vector or factor giving the experimental group/condition for each library.

lib.size

numeric vector giving the total count (sequence depth) for each library.

...

other arguments that are not currently used.

Value

• equalizeLibSizes.DGEList returns a DGEList object with the following new components:
• pseudo.counts
  numeric matrix of normalized pseudo-counts
• pseudo.lib.size
  normalized library size
• equalizeLibSizes.default returns a list with components {pseudo.counts} and {pseudo.lib.size}.

Details

Thus function implements the quantile-quantile normalization method of Robinson and Smyth (2008). It computes normalized counts, or pseudo-counts, used by exactTest and estimateCommonDisp. The output pseudo-counts are the counts that would have theoretically arisen had the effective library sizes been equal for all samples. The pseudo-counts are computed in such as way as to preserve fold-change differences beween the groups defined by y$samples$group as well as biological variability within each group. Consequently, the results will depend on how the groups are defined. Note that the column sums of the pseudo.counts matrix will not generally be equal, because the effective library sizes are not necessarily the same as actual library sizes and because the normalized pseudo counts are not equal to expected counts.

References

Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332. http://biostatistics.oxfordjournals.org/content/9/2/321

See Also

q2qnbinom

Examples

ngenes <- 1000
nlibs <- 2
counts <- matrix(0,ngenes,nlibs)
colnames(counts) <- c("Sample1","Sample2")
counts[,1] <- rpois(ngenes,lambda=10)
counts[,2] <- rpois(ngenes,lambda=20)
summary(counts)
y <- DGEList(counts=counts)
out <- equalizeLibSizes(y)
summary(out$pseudo.counts)