Genome-wide screens for effective siRNAs through assessing the size of siRNA effects

RNA interference (RNAi) has been seen as a revolution in functional genomics and system biology. Genome-wide RNAi research relies on the development of RNAi high-throughput screening (HTS) assays. One of the most fundamental challenges in RNAi HTS is to glean biological significance from mounds of data, which relies on the development of effective analytic methods for selecting effective small interfering RNAs (siRNAs).

Based on a recently proposed parameter, strictly standardized mean difference (SSMD), I propose an analytic method for genome-wide screens of effective siRNAs through assessing and testing the size of siRNA effects. Central to this method is the capability of SSMD in quantifying siRNA effects. This method has relied on normal approximation, which works only in the primary screens but not in the confirmatory screens. In this paper, I explore the non-central t-distribution property of SSMD estimates and use this property to extend the SSMD-based method so that it works effectively in either primary or confirmatory screens as well as in any HTS screens with or without replicates. The SSMD-based method maintains a balanced control of false positives and false negatives.

The central interest in genome-wide RNAi research is the selection of effective siRNAs which relies on the development of analytic methods to measure the size of siRNA effects. The new analytic method for hit selection provided in this paper offers a good analytic tool for selecting effective siRNAs, better than current analytic methods, and thus may have broad utility in genome-wide RNAi research.