MIST: Maximum Information Spanning Trees for dimension reduction of biological data sets.

TitleMIST: Maximum Information Spanning Trees for dimension reduction of biological data sets.
Publication TypeJournal Article
Year of Publication2009
AuthorsKing, BM, Tidor, B
Date Published2009 May 1
KeywordsComputational Biology, Databases, Genetic, Entropy, Gene Expression Profiling, Neoplasms, Software

MOTIVATION: The study of complex biological relationships is aided by large and high-dimensional data sets whose analysis often involves dimension reduction to highlight representative or informative directions of variation. In principle, information theory provides a general framework for quantifying complex statistical relationships for dimension reduction. Unfortunately, direct estimation of high-dimensional information theoretic quantities, such as entropy and mutual information (MI), is often unreliable given the relatively small sample sizes available for biological problems. Here, we develop and evaluate a hierarchy of approximations for high-dimensional information theoretic statistics from associated low-order terms, which can be more reliably estimated from limited samples. Due to a relationship between this metric and the minimum spanning tree over a graph representation of the system, we refer to these approximations as MIST (Maximum Information Spanning Trees).RESULTS: The MIST approximations are examined in the context of synthetic networks with analytically computable entropies and using experimental gene expression data as a basis for the classification of multiple cancer types. The approximations result in significantly more accurate estimates of entropy and MI, and also correlate better with biological classification error than direct estimation and another low-order approximation, minimum-redundancy-maximum-relevance (mRMR).AVAILABILITY: Software to compute the entropy approximations described here is available as Supplementary Material.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Alternate JournalBioinformatics
PubMed ID19261718
PubMed Central IDPMC2672626
Grant ListR01 GM065418-07 / GM / NIGMS NIH HHS / United States
R01 GM082209-03 / GM / NIGMS NIH HHS / United States
R01 GM082209-04 / GM / NIGMS NIH HHS / United States
T32 GM008334 / GM / NIGMS NIH HHS / United States
U54 CA112967 / CA / NCI NIH HHS / United States