A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data.

ABSTRACT: The RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels. Since the RNA-seq measurements are relative in nature, between-sample normalization is an essential step in differential expression (DE) analysis. The normalization step of existing DE detection algorithms is usually ad hoc and performed only once prior to DE detection, which may be suboptimal since ideally normalization should be based on non-DE genes only and thus coupled with DE detection. We propose a unified statistical model for joint normalization and DE detection of RNA-seq data. Sample-specific normalization factors are modeled as unknown parameters in the gene-wise linear models and jointly estimated with the regression coefficients. By imposing sparsity-inducing L1 penalty (or m

SUBMITTER: Liu K 

PROVIDER: S-EPMC6686202 | biostudies-literature | 2019 Mar-Apr

REPOSITORIES: biostudies-literature