Stability of gene rankings from RNAi screens

Abstract

MOTIVATION: Genome-wide RNA interference (RNAi) experiments are becoming a widely used approach for identifying intracellular molecular pathways of specific functions. However, detecting all relevant genes involved in a biological process is challenging, because typically only few samples per gene knock-down are available and readouts tend to be very noisy. We investigate the reliability of top scoring hit lists obtained from RNAi screens, compare the performance of different ranking methods, and propose a new ranking method to improve the reproducibility of gene selection.
RESULTS: The performance of different ranking methods is assessed by the size of the stable sets they produce, i.e. the subsets of genes which are estimated to be re-selected with high probability in independent validation experiments. Using stability selection, we also define a new ranking method, called stability ranking, to improve the stability of any given base ranking method. Ranking methods based on mean, median, t-test and rank-sum test, and their stability-augmented counterparts are compared in simulation studies and on three microscopy image RNAi datasets. We find that the rank-sum test offers the most favorable trade-off between ranking stability and accuracy and that stability ranking improves the reproducibility of all and the accuracy of several ranking methods.
AVAILABILITY: Stability ranking is freely available as the R/Bioconductor package staRank at http://www.cbg.ethz.ch/software/staRank.