Robust statistical methods for hit selection in RNA interference high-throughput screening experiments

RNA interference (RNAi) high-throughput screening (HTS) experiments carried out using large (>5000 short interfering [si]RNA) libraries generate a huge amount of data. In order to use these data to identify the most effective siRNAs tested, it is critical to adopt and develop appropriate statistical methods. To address the questions in hit selection of RNAi HTS, we proposed a quartile-based method which is robust to outliers, true hits and nonsymmetrical data. We compared it with the more traditional tests, mean ± k standard deviation (SD) and median ± 3 median of absolute deviation (MAD). The results suggested that the quartile-based method selected more hits than mean ± k SD under the same preset error rate. The number of hits selected by median ± k MAD was close to that by the quartile-based method. Further analysis suggested that the quartile-based method had the greatest power in detecting true hits, especially weak or moderate true hits. Our investigation also suggested that platewise analysis (dete...