A Permutation-Based Framework for Accurate Predication of Differentially Expressed Genes

• Activity Number: 427
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Weichun Huang*
• Companies: NIH/NIEHS
• Keywords: permutation ; gene expression ; sequencing ; differentially expressed genes

Abstract

Cost-effective RNA-seq has quickly become a popular method for studying gene expression and their regulation mechanisms. The development of analysis methods, however, lags far behind the need. Particularly, existing tools are no longer sufficient for many new and more advanced sequencing applications. For example, many early tools, designed for data with few replicates, cannot take full advantage of sequencing data with more replicates and/or more complex experimental designs; and some of these tools report inaccurate significant assessment as they rely on some unrealistic assumptions. To address these issues, we introduce a new statistical framework for analyzing RNA-seq data with 2 or more biological replicates. Our framework first calculates a test statistic for each gene and then uses permutation-based method to assess significance of the test statistic of each gene. Furthermore, our framework can employ different pre-filters and statistical tests for data analysis, based on the number of available biological replicates, and the study's experimental design. Evaluation of our framework on both simulated and actual sequencing data will be presented and discussed.