A Bayesian Measurement Error Model for Two-Channel Cell-Based RNAi Data with Replicates

• Activity Number: 378
• Type: Invited
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): I-Shou Chang*+ and Chung-Hsing Chen and Chao Hsiung and King-Song Jeng
• Companies: National Health Research Institutes and National Health Research Institutes and National Health Research Institutes and Academia Sinica
• Keywords: Bayesian models ; HCV replication ; High-throughput screening ; Multiple hypothesis testing ; RNA interference ; Viral-host interaction

Abstract

RNA interference (RNAi) is an endogenous cellular process in which small double-stranded RNAs lead to the destruction of mRNAs with complementary nucleoside sequence. With the production of RNAi libraries, large-scale RNAi screening in hu- man cells can be conducted to identify unknown genes involved in a biological pathway. One challenge in these studies is to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). We propose a Bayesian measurement error model for the analysis of data from a two-channel RNAi high-throughput experiment with replicates, in which both the activity of a particular bio logical pathway and cell viability are monitored and the goal is to identify shRNAs that affect the pathway activity without affecting cell activity. Simulation studies indicate the excellent numerical performance of this method and provide insight into the effects of prior distributions and the number of replicates on FDR and FNR. This method is illustrated in analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability