Abstract
Measuring associations is an important scientific task. A novel measurement method maximal information coefficient (MIC) was proposed to identify a broad class of associations. As foreseen by its authors, MIC implementation algorithm ApproxMaxMI is not always convergent to real MIC values. An algorithm called SG (Simulated annealing and Genetic) was developed to facilitate the optimal calculation of MIC, and the convergence of SG was proved based on Markov theory. When run on fruit fly data set including 1,000,000 pairs of gene expression profiles, the mean squared difference between SG and the exhaustive algorithm is 0.00075499, compared with 0.1834 in the case of ApproxMaxMI. The software SGMIC and its manual are freely available at http://lxy.depart.hebust.edu.cn/SGMIC/SGMIC.htm.
Highlights
Measuring associations is an important scientific task
maximal information coefficient (MIC) does not rely on the distributional assumptions of measured data and could identify a broad class of associations compared with previous studies
ApproxMaxMI does not optimize the partition of the y-axis
Summary
A novel measurement method maximal information coefficient (MIC) was proposed to identify a broad class of associations. Relationships and associations should be identified and measured to explore the rules of development. A typical example is measuring the relationships between genes by determining the associations between their expression profiles[4,5]. Many methods have been developed to measure associations through calculation of correlation coefficients, such as Pearson’s, Spearman’s, mutual information[6,7], CorGC8, and maximal correlation[9]. Reshef et al.[10] proposed a novel correlation measurement ‘‘maximal information coefficient’’ (MIC), and gave a 1-D dynamic programming algorithm, ApproxMaxMI, to calculate MIC. MIC does not rely on the distributional assumptions of measured data and could identify a broad class of associations compared with previous studies.
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