Optimizing murine sample sizes for RNA-seq studies revealed from large-scale comparative analysis
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Determining the appropriate sample size (N) for comparative biological experiments is critical for obtaining reliable results. In order to determine the N, the usual approach is to perform a power calculation, which involves understanding the variability between samples and the expected effect size. Here, we focused on bulk RNA-seq experiments, which have become ubiquitous in biology, but which have many unknown or difficult to estimate parameters, and so the required analyses to determine the minimum N is typically lacking. We therefore performed two N=30 profiling studies between wild-type mice and mice in which one copy of a gene had been deleted, to determine how many mice would be required to minimize false positives and to maximize true discoveries found in the N of 30 experiment. Results from experiments with N=4 or less are shown to be highly misleading, given the substantial false positive rate, and the lack of discovery of genes later found with higher N. For a cut-off of 2-fold expression differences, we found that an N of 6-7 mice was required to consistently decrease the false positive rate to below 50%, and that “more is always better” when it came to discovery rates - an N of 8-12 is significantly better in lowering the false positive rate.
A common method to reduce false discovery rate in underpowered experiments is to raise the fold cutoff or increase the stringency of the P-value and include only highly perturbed, highly significant genes. We show that while this strategy is no substitute for increasing the N of the experiment, because it results in consistently inflated effect sizes and a substantial drop in sensitivity. These data should be helpful to others in choosing their Ns, since it’s often not practical to do such large studies for every mouse model.