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. 2008 Aug;36(14):4667-79.
doi: 10.1093/nar/gkn435. Epub 2008 Jul 15.

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

Xiaohua Douglas Zhang  1 , 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
Affiliations

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

Xiaohua Douglas Zhang et al. Nucleic Acids Res. 2008 Aug.

Abstract

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 +/- kMAD 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.

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Figures

Figure 1.
Figure 1.
Hits identified by Bayesian methods with FDR control. In each panel, green, red, light pink, grey, yellow, blue and light blue points represent a negative control, moderate inhibition control, strong inhibition control, fairly strong activation control, nonhits, activation hits and inhibition hits, respectively. A and B show the result using priors based on the negative control only; C and D show the results using priors based on the negative, activation and moderate inhibition controls and E and F show the results using priors based on the negative, activation and strong inhibition controls.
Figure 2.
Figure 2.
Hit selection results in the HIV screen, identified by median ± 3MAD plate wise (A and B) or experiment wise (C and D) and using the majority of sample wells (A and C) or a negative control (B and D) as a negative reference. In each panel, green, red, light pink, grey, yellow, blue and light blue points represent a negative control, moderate inhibition control, strong inhibition control, activation control, nonhits, activation hits and inhibition hits, respectively.
Figure 3.
Figure 3.
Hit selection results in the HCV screen, identified by median ± 3MAD plate wise (A) and experiment wise (B) using the majority of sample wells as a negative reference as well as by Bayesian Model 1 (C and D) and Bayesian Model 2 with the weaker inhibition control (E and F). In a panel, green, red, light pink, grey, yellow, blue and light blue points represent a negative control, weaker inhibition control, stronger inhibition control, pseudo activation control, nonhits, activation hits and inhibition hits, respectively.
Figure 4.
Figure 4.
ROC curves for detecting all three positive controls in the HIV siRNA screen (A) and for detecting two inhibition controls in the HCV siRNA screen (B). The negative control in each screen is used to calculate the specificity in the screen.
Figure 5.
Figure 5.
Simulated data and results. A1 shows a simple realization of 100 plates in 1 run of the first simulation with h = 1 where formula image. In this panel, green, red, gray and yellow points represent a negative control, inhibition control, activation control and nonhits, respectively; blue and light blue crosses represent activation and inhibition hits, respectively. A2 shows the ROC curves by applying four methods to the data in A1. B1−B5 (and C1−C5) show AUROC's for the specificity between 1 and 0.95 in the first (and second) simulation study with h = 1, 2, 3, 4 and 5, respectively. In each A1, B1–B5 and C1–C5, the Bayesian models 1–2, plate-wise and experiment-wise median ± kMAD are labeled as BayesM1 (red), BayesM2 (green), PLT wise (blue) and EXP wise (light blue), respectively.
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