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

Jianlong Qi, Tom Michoel

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)2325-2332
Number of pages8
JournalBioinformatics
Volume28
Issue number18
DOIs
Publication statusPublished - 15 Sep 2012
EventECCB'12 - the European Conference on Computational Biology - Basel, Switzerland
Duration: 9 Sep 201212 Sep 2012

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