transformRNAseq: Transform RNA-seq data using common transformations

In coseq: Co-Expression Analysis of Sequencing Data

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

coseq documentation built on Nov. 8, 2020, 5:18 p.m.