A practical tool for maximal information coefficient analysis

BACKGROUND: The ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual information-based measures of association are particularly promising, in particular after the recent introduction of the TIC(e) and MIC(e) estimators, which combine computational efficiency with superior bias/variance properties. An open-source software implementation of these two measures providing a complete procedure to test their significance would be extremely useful. FINDINGS: Here, we present MICtools, a comprehensive and effective pipeline that combines TIC(e) and MIC(e) into a multistep procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated. CONCLUSIONS: We show that MICtools, combining TIC(e) and MIC(e), is able to highlight associations that would not be captured by conventional strategies.