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

TL;DR

This paper introduces an adaptive statistical method for detecting direct gene targets in perturbation experiments, improving detection accuracy and stability by modeling small effects and providing theoretical guarantees.

Contribution

It proposes a novel adaptive approach using three estimators for variance, with theoretical analysis and superior performance over existing methods in simulations and real data.

Findings

The iterated empirical Bayes estimator performs best among the three methods.

The new approach outperforms existing methods in simulation studies.

Application to real gene knock-down data yields improved detection of direct targets.

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.