Statistical Methods for Analysis of High-Throughput RNA Interference Screens

By Amanda Birmingham, L. Selfors, T. Forster, D. Wrobel, C. J. Kennedy, Emma Shanks, Javier Santoyo-Lopez, D. Dunican, A. Long, D. Kelleher and 4 more. Nature Methods, 2009

604 citations recorded by the source corpus.

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6e5133d8f7b7ccdd0781658049ce1603bcbbbf92

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In Molecular Biology

Citations in the corpus

Data-analysis strategies for image-based cell profiling (2017, background): “A support vector machine is trained to implicitly weigh useful features in a classification task.”
Multi-Domain Adversarial Learning (2019, methodology): “Batch effects in image-based screening data are usually tackled with speciﬁc normalization methods (Birmingham et al., 2009).”
Opportunities and Challenges for use of Tumor Spheroids as Models to Test Drug Delivery and Efficacy (2012, background): “Z-factor, an assay performance measurement that provides an easy and useful summary of assay quality and robustness [103–105], was found to be well above 0.”