  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.
