Hit selection with false discovery rate control in genome-scale RNAi screens

Xiaohua Douglas Zhang, Pei Fen Kuan, Marc Ferrer, Xiaohua Shu, Yingxue C. Liu, Adam T. Gates, Priya Kunapuli, Erica M. Stec, Min Xu, Shane D. Marine, Daniel J. Holder, Berta Strulovici, Joseph F. Heyse, Amy S. Espeseth

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


RNA interference (RNAi) is a modality in which small double-stranded RNA molecules (siRNAs) designed to lead to the degradation of specific mRNAs are introduced into cells or organisms. siRNA libraries have been developed in which siRNAs targeting virtually every gene in the human genome are designed, synthesized and are presented for introduction into cells by transfection in a microtiter plate array. These siRNAs can then be transfected into cells using high-throughput screening (HTS) methodologies. The goal of RNAi HTS is to identify a set of siRNAs that inhibit or activate defined cellular phenotypes. The commonly used analysis methods including median ± k MAD have issues about error rates in multiple hypothesis testing and plate-wise versus experiment-wise analysis. We propose a methodology based on a Bayesian framework to address these issues. Our approach allows for sharing of information across plates in a plate-wise analysis, which obviates the need for choosing either a plate-wise or experimental-wise analysis. The proposed approach incorporates information from reliable controls to achieve a higher power and a balance between the contribution from the samples and control wells. Our approach provides false discovery rate (FDR) control to address multiple testing issues and it is robust to outliers.

Original languageEnglish
Pages (from-to)4667-4679
Number of pages13
JournalNucleic Acids Research
Issue number14
StatePublished - Aug 2008

ASJC Scopus subject areas

  • Genetics


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