Work

A comparative study of statistical methods used to identify dependencies between gene expression signals

Year: 2013

Type: article

Abstract: One major task in molecular biology is to understand the dependency among genes to model gene regulatory networks. Pearson's correlation is the most common method used to measure dependence between gene expression signals, but it works well only when data are linearly associated. For other types of association, such as non-linear or non-functional relationships, methods based on the concepts of rank correlation and information theory-based measures are more adequate than the Pearson's correlation, but are less used in applications, most probably because of a lack of clear guidelines for their use. This work seeks to summarize the main methods (Pearson's, Spearman's and Kendall's correlations; distance correlation; Hoeffding's D: measure; Heller-Heller-Gorfine measure; mutual information and maximal information coefficient) used to identify dependency between random variables, especially gene expression data, and also to evaluate the strengths and limitations of each method. Systematic Monte Carlo simulation analyses ranging from sample size, local dependence and linear/non-linear and also non-functional relationships are shown. Moreover, comparisons in actual gene expression data are carried out. Finally, we provide a suggestive list of methods that can be used for each type of data set.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bbt051

Authors: S. de Siqueira Santos, D. Y. Takahashi, A. Nakata, A. Fujita

Institutions: Universidade de São Paulo, Princeton University

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Japan Society for the Promotion of Science

Awards: Models and methods of e-Science for life and agricultural sciences, Novel molecular mechanisms of acquired resistance to gefitinib in lung adenocarcinoma, Granger causality for sets of time series: development of methodologies to model selection and extensions in the frequency domain with applications to molecular biology and neuroscience, Combinatorial structures, optimization, and algorithms in theoretical Computer Science