Inflated false discovery rate due to volcano plots: problem and solutions

Motivation: Volcano plots are used to select the most interesting discoveries when too many discoveries remain after application of Benjamini-Hochberg's procedure (BH). The volcano plot suggests a double filtering procedure that selects features with both small adjusted -value and large estimated effect size. Despite its popularity, this type of selection overlooks the fact that BH does not guarantee error control over filtered subsets of discoveries. Therefore the selected subset of features may include an inflated number of false discoveries. Results: In this paper, we illustrate the substantially inflated type I error rate of volcano plot selection with simulation experiments and RNA-seq data. In particular, we show that the feature with the largest estimated effect is a very likely false positive result. Next, we investigate two alternative approaches for multiple testing with double filtering that do not inflate the false discovery rate. Our procedure is implemented in an interactive web application and is publicly available.

Automated summary

Volcano plots are commonly used to select interesting discoveries, but they may lead to inflated false discovery rates. We demonstrate this issue with simulation experiments and data, and propose alternative approaches for multiple testing that do not inflate the false discovery rate.