A powerful and flexible approach to the analysis of RNA sequence count data.

ABSTRACT:

Motivation

A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean-variance relationships provides a flexible testing regimen that 'borrows' information across genes, while easily incorporating design effects and additional covariates.

Results

We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean-variance modeling approach to RNA-Seq data, in

SUBMITTER: Zhou YH 

PROVIDER: S-EPMC3179656 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature