title: Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests.
abstract: BACKGROUND
  Transcriptional regulatory network inference methods have been studied for years. Most of them rely 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 Escherichia 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 >1000 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.
  
  
  BACKGROUND
  A software tool called TwixTrix is available from http://twixtrix.googlecode.com.
  
  
  BACKGROUND
  Supplementary Material is available from http://www.roslin.ed.ac.uk/tom-michoel/supplementary-data.
  
  
  BACKGROUND
  tom.michoel@roslin.ed.ac.uk.
authors.affiliation: School of Life Sciences-LifeNet, Freiburg Institute for Advanced Studies, University of Freiburg, Albertstrasse 19, D-79104 Freiburg im Breisgau, Germany.
authors.forename: Jianlong
authors.initials: J
authors.lastname: Qi
authors.forename: Tom
authors.initials: T
authors.lastname: Michoel
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D015233:Models, Statistical; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors