Open Access
March 2012 A Bayesian measurement error model for two-channel cell-based RNAi data with replicates
Chung-Hsing Chen, Wen-Chi Su, Chih-Yu Chen, Jing-Ying Huang, Fang-Yu Tsai, Wen-Chang Wang, Chao A. Hsiung, King-Song Jeng, I-Shou Chang
Ann. Appl. Stat. 6(1): 356-382 (March 2012). DOI: 10.1214/11-AOAS496


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 human cells can be conducted to identify unknown genes involved in a biological pathway. One challenge researchers face is how to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). This paper proposes a Bayesian hierarchical 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 biological pathway and cell viability are monitored and the goal is to identify short hair-pin RNAs (shRNAs) that affect the pathway activity without affecting cell activity. Simulation studies demonstrate the flexibility and robustness of the Bayesian method and the benefits of having replicates in the experiment. This method is illustrated through analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability; comparisons of the results from this HCV study and some of those reported in the literature are included.


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Chung-Hsing Chen. Wen-Chi Su. Chih-Yu Chen. Jing-Ying Huang. Fang-Yu Tsai. Wen-Chang Wang. Chao A. Hsiung. King-Song Jeng. I-Shou Chang. "A Bayesian measurement error model for two-channel cell-based RNAi data with replicates." Ann. Appl. Stat. 6 (1) 356 - 382, March 2012.


Published: March 2012
First available in Project Euclid: 6 March 2012

zbMATH: 1235.62027
MathSciNet: MR2951541
Digital Object Identifier: 10.1214/11-AOAS496

Keywords: Bayesian hierarchical models , HCV replication , high-throughput screening , multiple hypothesis tests , RNA interference , viral-host interactions

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 1 • March 2012
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