Motivation: 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 is conceptually simple, easy to implement and integrate in any statistical software environment, and equally well performing as existing methods.
Results: We developed a method to infer regulatory interactions based on a model where 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. Benchmarking on standard E. coli and yeast reference datasets showed that this method 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 coexpression method only associates general TFs to non-specific biological processes.
Availability: A software tool called TwixTrix is available from http://omics.frias.unifreiburg. de/software. Supplementary Material is available from http://omics.frias.unifreiburg. de/supplementary-data.