  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.
