Pathway Analysis Using a Score-Based Approach for RNA-Seq Data

• Activity Number: 604
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Yihui Zhou*+ and Fred Wright
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
• Keywords: RNA-seq ; pathway analysis ; statistical genetics ; linear model

Abstract

A variety of pathway/gene-set approaches have been proposed to provide evidence of higher-level biological phenomena in the association of expression with experimental condition or clinical outcome. Among these approaches, it has been repeatedly shown that permutation or bootstrapping of samples is far preferable to approaches that implicitly assume independence of genes. However, few approaches have been optimized for the specific characteristics of RNA-Seq transcription data, in which mapped tags produce discrete counts, and for which library sizes or other normalization factors may vary across samples. We describe transformations to RNA-Seq data to provide high power for linear associations with outcome and flexibly handle normalization factors. With these transformations, we can apply the recently-developed safeExpress approach to expression-based pathway analysis, which uses score-based gene-specific statistics with aggregated summaries across gene sets to represent pathway evidence. We demonstrate that the approach provides appropriate type I error control without permutation, and provides a convenient integrated approach to RNA-Seq pathway analysis.