Median absolute deviation to improve hit selection for genome-scale RNAi screens.
High-throughput screening (HTS) of large-scale RNA interference (RNAi) libraries has become an increasingly popular method of functional genomics in recent years. Cell-based assays used for RNAi screening often produce small dynamic ranges and significant variability because of the combination of cellular heterogeneity, transfection efficiency, and the intrinsic nature of the genes being targeted. These properties make reliable hit selection in the RNAi screen a difficult task. The use of robust methods based on median and median absolute deviation (MAD) has been suggested to improve hit selection in such cases, but mean and standard deviation (SD)-based methods are still predominantly used in many RNAi HTS. In an experimental approach to compare these 2 methods, a genome-scale small interfering RNA (siRNA) screen was performed, in which the identification of novel targets increasing the therapeutic index of the chemotherapeutic agent mitomycin C (MMC) was sought. MAD values were resistant to the presence of outliers, and the hits selected by the MAD-based method included all the hits that would be selected by SD-based method as well as a significant number of additional hits. When retested in triplicate, a similar percentage of these siRNAs were shown to genuinely sensitize cells to MMC compared with the hits shared between SD- and MAD-based methods. Confirmed hits were enriched with the genes involved in the DNA damage response and cell cycle regulation, validating the overall hit selection strategy. Finally, computer simulations showed the superiority and generality of the MAD-based method in various RNAi HTS data models. In conclusion, the authors demonstrate that the MAD-based hit selection method rescued physiologically relevant false negatives that would have been missed in the SD-based method, and they believe it to be the desirable 1st-choice hit selection method for RNAi screen results.
Chung
Namjin
N
Department of Automated Biotechnology, Merck Research Laboratories, North Wales, PA 19454, USA. namjin.chung@bms.com
Zhang
Xiaohua Douglas
XD
Kreamer
Anthony
A
Locco
Louis
L
Kuan
Pei-Fen
PF
Bartz
Steven
S
Linsley
Peter S
PS
Ferrer
Marc
M
Strulovici
Berta
B
D000903:Antibiotics, Antineoplastic / Q000494:pharmacology; D001331:Automation; D002470:Cell Survival / Q000187:drug effects / Q000235:genetics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D019008:Drug Resistance, Neoplasm / Q000187:drug effects / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D015894:Genome, Human / Q000187:drug effects; D023281:Genomics / Q000379:methods*; D006367:HeLa Cells; D006801:Humans; D016685:Mitomycin / Q000494:pharmacology; D034741:RNA, Small Interfering / Q000494:pharmacology*; D012107:Research Design; D014162:Transfection
D000903:Antibiotics, Antineoplastic; D034741:RNA, Small Interfering; D016685:Mitomycin