Analysis of small-sample clinical genomics studies using multi-parameter shrinkage: application to high-throughput RNA interference screening

High-throughput (HT) RNA interference (RNAi) screens are increasingly used for reverse genetics and drug discovery. These experiments are laborious and costly, hence sample sizes are often very small. Powerful statistical techniques to detect siRNAs that potentially enhance treatment are currently lacking, because they do not optimally use the amount of data in the other dimension, the feature dimension. We introduce ShrinkHT, a Bayesian method for shrinking multiple parameters in a statistical model, where 'shrinkage' refers to borrowing information across features. ShrinkHT is very flexible in fitting the effect size distribution for the main parameter of interest, thereby accommodating skewness that naturally occurs when siRNAs are compared with controls. In addition, it naturally down-weights the impact of nuisance parameters (e.g. assay-specific effects) when these tend to have little effects across siRNAs. We show that these properties lead to better ROC-curves than with the popular limma software. Moreover, in a 3 + 3 treatment vs control experiment with 'assay' as an additional nuisance factor, ShrinkHT is able to detect three (out of 960) significant siRNAs with stronger enhancement effects than the positive control. These were not detected by limma. In the context of gene-targeted (conjugate) treatment, these are interesting candidates for further research.