Statistical methods for analysis of high-throughput RNA interference screens

Abstract

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

Figure 1

Methods for visualizing data outputs of a screen. (a) heat map of raw values from a hypothetical 384-well plate with strong edge effects; (b) replicate correlation plots of raw values from two hypothetical replicates of the same master plate showing good agreement and suggesting overall good reproducibility; and (c) plate-well scatter plot of raw values from a hypothetical screen of eighteen 96-well plates including evidence of drift in the first three plates and a plate with unusually low values near the end of the screen.