Detecting novel associations in large data sets.

Journal article

Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R(2)) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

Authors: Reshef, D; Reshef, Y; Finucane, H; Grossman, SR; McVean, G

• Publication status: Published
• Journal: Science (New York, N.Y.)
• Volume: 334
• Issue: 6062
• Pages: 1518-1524
• Publication date: 2011-12-01
• DOI: 10.1126/science.1205438
• EISSN: 1095-9203
• ISSN: 0036-8075
• Language: English
• Keywords: Metagenome; Algorithms; Intestines; Genes, Fungal; Female; Genomics; Humans; Male; Data Interpretation, Statistical; Animals; Gene Expression; Saccharomyces cerevisiae; Obesity; Mice; Baseball