Motivation: To improve the understanding of molecular regulation events, various approaches have been developed for deducing gene regulatory networks from mRNA expression data.
Results: We present a new score for network inference, η(2), that is derived from an analysis of variance. Candidate transcription factor:target gene (TF:TG) relationships are assumed more likely if the expression of TF and TG are mutually dependent in at least a subset of the examined experiments. We evaluate this dependency by η(2), a non-parametric, non-linear correlation coefficient. It is fast, easy to apply and does not require the discretization of the input data. In the recent DREAM5 blind assessment, the arguably most comprehensive evaluation of inference methods, our approach based on η(2) was rated the best performer on real expression compendia. It also performs better than methods tested in other recently published comparative assessments. About half of our predicted novel predictions are true interactions as estimated from qPCR experiments performed for DREAM5.
Conclusions: The score η(2) has a number of interesting features that enable the efficient detection of gene regulatory interactions. For most experimental setups, it is an interesting alternative to other measures of dependency such as Pearson's correlation or mutual information.