Detecting strong signals in gene perturbation experiments: An adaptive approach with power guarantee and FDR control

Abstract

The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose a modified two-group model where the null group corresponds to genes which are not direct targets, but can have small non-zero effects. We model the behaviour of genes from the null set by a Gaussian distribution with unknown variance $\tau^2$, and we discuss and compare three methods which adaptively estimate $\tau^2$ from the data: the iterated empirical Bayes estimator, the truncated MLE and the central moment matching estimator. We conduct a detailed analysis of the properties of the iterated EB estimate which has the best performance in the simulations. In particular, we provide theoretical guarantee of its good performance under mild conditions. We provide simulations comparing the new modeling approach with existing methods, and the new approach shows more stable and better performance under different situations. We also apply it to a real data set from gene knock-down experiments and obtained better results compared with the original two-group model testing for non-zero effects.