transformRNAseq {coseq}R Documentation

Transform RNA-seq data using common transformations

Description

Application of common transformations for RNA-seq data prior to fitting a normal mixture model

Usage

transformRNAseq(y, normFactors = "TMM", transformation = "arcsin",
  geneLength = NA, meanFilterCutoff = NULL, verbose = TRUE)

Arguments

y

(n x q) matrix or data.frame of observed counts for n observations and q variables

normFactors

The type of estimator to be used to normalize for differences in library size: “TC” for total count, “DESeq” for the normalization method in the DESeq package, and “TMM” for the TMM normalization method (Robinson and Oshlack, 2010). Can also be a vector (of length q) containing pre-estimated library size estimates for each sample.

transformation

Transformation type to be used: “arcsin”, “logit”, “logMedianRef”, “profile”, “voom”, “logRPKM” (if geneLength is provided by user), “logclr”, “clr”, “alr”, “ilr”, “none”,

geneLength

Vector of length equal to the number of rows in “y” providing the gene length (bp) for RPKM calculation

meanFilterCutoff

Value used to filter low mean normalized counts

verbose

If TRUE, include verbose output

Value

tcounts

Transformed counts

normCounts

Normalized counts

snorm

Per-sample normalization factors divided by mean normalization factor

ellnorm

Per-sample normalization factors

Examples

set.seed(12345)
countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
countmat <- countmat[which(rowSums(countmat) > 0),]
conds <- rep(c("A","B","C","D"), each=2)

## Arcsin transformation, TMM normalization
arcsin <- transformRNAseq(countmat, normFactors="TMM", transformation="arcsin")$tcounts
## Logit transformation, TMM normalization
logit <- transformRNAseq(countmat, normFactors="TMM", transformation="logit")$tcounts
## logCLR transformation, TMM normalization
logclr <- transformRNAseq(countmat, normFactors="TMM", transformation="logclr")$tcounts
[Package coseq version 1.12.0 Index]