Abstract
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.
Highlights
Many clinical genomics studies suffer from low power and low reproducibility caused by small sample sizes
We showed its improved performance in terms of Receiver Operating Characteristic (ROC)-curves with respect to other methods like edgeR, baySeq and van de Wiel et al BMC Medical Genomics 2013, 6(Suppl 2):S1 http://www.biomedcentral.com/1755-8794/6/S2/S1
Successful estimation of the priors To assess whether priors can be successfully estimated in this very challenging setting with only 6 measurements per small interference RNAs (siRNAs), two conditions and the presence of a threelevel nuisance factor, we set up a simulation that is strongly motivated from the data: the model is the same as (2) except for the offset which is not relevant here
Summary
Many clinical genomics studies suffer from low power and low reproducibility caused by small sample sizes. Small sample sizes may be due to high costs per sample, low availability of genomic material (e.g. for rare diseases) or even juridical restrictions (e.g. when administering an experimental drug to patients). The philosophy behind our method is to increase power and reproducibility by retrieving as much information as possible from the vertical data direction (feature space: genes, tags, small interference RNAs (siRNAs), etc.) for estimating differential treatment effects from the horizontal data direction (sample space). In statistical terms the latter is referred to as ‘shrinkage’. We showed its improved performance in terms of Receiver Operating Characteristic (ROC)-curves with respect to other methods like edgeR, baySeq and van de Wiel et al BMC Medical Genomics 2013, 6(Suppl 2):S1 http://www.biomedcentral.com/1755-8794/6/S2/S1
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