transformRNAseq {coseq} | R Documentation |
Application of common transformations for RNA-seq data prior to fitting a normal mixture model
transformRNAseq( y, normFactors = "TMM", transformation = "arcsin", geneLength = NA, meanFilterCutoff = NULL, verbose = TRUE )
y |
(n x q) |
normFactors |
The type of estimator to be used to normalize for differences in
library size: “ |
transformation |
Transformation type to be used: “ |
geneLength |
Vector of length equal to the number of rows in “ |
meanFilterCutoff |
Value used to filter low mean normalized counts |
verbose |
If |
tcounts |
Transformed counts |
normCounts |
Normalized counts |
snorm |
Per-sample normalization factors divided by mean normalization factor |
ellnorm |
Per-sample normalization factors |
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