An effective method for controlling false discovery and false nondiscovery rates in genome-scale RNAi screens.
In most genome-scale RNA interference (RNAi) screens, the ultimate goal is to select siRNAs with a large inhibition or activation effect. The selection of hits typically requires statistical control of 2 errors: false positives and false negatives. Traditional methods of controlling false positives and false negatives do not take into account the important feature in RNAi screens: many small-interfering RNAs (siRNAs) may have very small but real nonzero average effects on the measured response and thus cannot allow us to effectively control false positives and false negatives. To address for deficiencies in the application of traditional approaches in RNAi screening, the author proposes a new method for controlling false positives and false negatives in RNAi high-throughput screens. The false negatives are statistically controlled through a false-negative rate (FNR) or false nondiscovery rate (FNDR). FNR is the proportion of false negatives among all siRNAs examined, whereas FNDR is the proportion of false negatives among declared nonhits. The author also proposes new concepts, q*-value and p*-value, to control FNR and FNDR, respectively. The proposed method should have broad utility for hit selection in which one needs to control both false discovery and false nondiscovery rates in genome-scale RNAi screens in a robust manner.
Zhang
Xiaohua Douglas
XD
Biometrics Research, Merck Research Laboratories, West Point, PA 19486, USA. xiaohua_zhang@merck.com
D005188:False Negative Reactions; D005189:False Positive Reactions; D016678:Genome / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000378:metabolism
D034741:RNA, Small Interfering