Work

Sample size calculation for differential expression analysis of RNA-seq data under Poisson distribution

Year: 2013

Type: article

Abstract: Sample size determination is an important issue in the experimental design of biomedical research. Because of the complexity of RNA-seq experiments, however, the field currently lacks a sample size method widely applicable to differential expression studies utilising RNA-seq technology. In this report, we propose several methods for sample size calculation for single-gene differential expression analysis of RNA-seq data under Poisson distribution. These methods are then extended to multiple genes, with consideration for addressing the multiple testing problem by controlling false discovery rate. Moreover, most of the proposed methods allow for closed-form sample size formulas with specification of the desired minimum fold change and minimum average read count, and thus are not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size formulas are presented; the results indicate that our methods work well, with achievement of desired power. Finally, our sample size calculation methods are applied to three real RNA-seq data sets.

Source: International Journal of Computational Biology and Drug Design

DOI: 10.1504/ijcbdd.2013.056830

Authors: Chung I Li, Pei Fang Su, Yan Guo, Yu Shyr

Institutions: National Chiayi University, National Cheng Kung University, Vanderbilt University

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Cancer Institute

Awards: Barrett's Esophagus Translational Research Network Coordinating Center, SPORE in GI Cancer, SPORE in Breast Cancer, Cancer Center Support Grant