Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests

TL;DR

This paper introduces a simple, statistically grounded method for inferring context-specific transcriptional regulatory networks from gene expression data, demonstrating comparable performance to existing methods and highlighting its ability to identify tissue-specific interactions.

Contribution

The authors present a minimal statistical model based on two-way t-tests for network inference, which is easy to implement and effective in detecting context-specific transcription factor interactions.

Findings

Performs as well as top existing methods on benchmark datasets.

Effectively identifies tissue-specific transcriptional interactions.

Reveals that local co-expression better indicates functional regulation.

Abstract

Transcriptional regulatory network inference methods have been studied for years. Most of them relie on complex mathematical and algorithmic concepts, making them hard to adapt, re-implement or integrate with other methods. To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which assumes that transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. This method is conceptually simple and easy to implement and integrate in any statistical software environment. Benchmarking on standard E. coli and yeast reference datasets showed that it performs equally well as the best existing methods. Analysis of the predicted interactions suggested that it works best to infer context-specific TF-target interactions which only co-express locally. We confirmed this hypothesis on a dataset of more than 1,000 normal human tissue samples, where we found that our method predicts highly tissue-specific and functionally relevant interactions, whereas a global co-expression method only associates general TFs to non-specific biological processes.