A Bayesian measurement error model for two-channel cell-based RNAi data with replicates

TL;DR

This paper introduces a Bayesian hierarchical measurement error model for analyzing two-channel RNAi high-throughput data with replicates, improving detection of gene effects on pathways while controlling false discovery rates.

Contribution

It presents a novel Bayesian approach tailored for two-channel RNAi data with replicates, addressing measurement errors and multiple testing issues.

Findings

Simulation studies show the method's robustness and flexibility.

Application to HCV data identifies relevant cellular factors.

Replicates enhance detection accuracy.

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 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.