1. Evaluating other shRNA data and methods.
Evaluating other shRNA data and methods. AAnalytical approach. CCE reference set was derived from the initial analysis; NE set is identical throughout. B, CEvaluating other RNAi data sets. (B) LOD scores were calculated for the pooled library shRNA screens in the HCT116 background in (Vizeacoumar et al, 2013) and evaluated against CCE‐test and NE‐test. Recall, TP/(TP+FN); Precision, TP/(TP+FP). All six screens showed very high accuracy. The filled circle indicates the point on the curve where LOD = 0. (C) LOD scores were calculated for the pooled library shRNA screens in 102 cancer cell lines in (Cheung et al, 2011). Blue points represent recall & precision at LOD = 0 as measured against CCE‐test and NE‐test. Red, recall and precision for the same cell lines and same reference sets from ATARiS gene solutions at phenotype score = −1. DIntegrating gene expression into the Bayesian classifier. For RNAi screens with matched gene expression data (in this example, PDAC cell line CAPAN‐2, black curve), genes are binned by expression level and the fraction of reference essentials in each bin (right y‐axis) is plotted against the mean expression of genes in the bin (green points). A linear fit on the log‐log plot (green dashed line) can be integrated into the Bayesian classifier as an informative prior. EIntegrating expression data improves the performance of the classifier (green) over the base algorithm (blue). Both forms show better performance than other algorithms such as GARP (red) and RIGER (gold).
Traver Hart et al. Mol Syst Biol 2014;10:733
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