The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 6, Number 1 (2012), 356-382.
A Bayesian measurement error model for two-channel cell-based RNAi data with replicates
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.
Ann. Appl. Stat., Volume 6, Number 1 (2012), 356-382.
First available in Project Euclid: 6 March 2012
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Chen, Chung-Hsing; Su, Wen-Chi; Chen, Chih-Yu; Huang, Jing-Ying; Tsai, Fang-Yu; Wang, Wen-Chang; Hsiung, Chao A.; Jeng, King-Song; Chang, I-Shou. A Bayesian measurement error model for two-channel cell-based RNAi data with replicates. Ann. Appl. Stat. 6 (2012), no. 1, 356--382. doi:10.1214/11-AOAS496. https://projecteuclid.org/euclid.aoas/1331043400
- Supplementary material: A Computer algorithm for analyzing data from two-channel cell-based RNAi experiments with replicates. This note provides the hybrid MCMC algorithm for sampling the posterior distribution used in Chen et al. (2011) and several observations used in designing this algorithm so as to make it more efficient.