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

Autor(es)

Santos, Suzana de Siqueira ; Takahashi, Daniel Yasumasa ; Nakata, Asuka ; Fujita, Andre ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Dept Comp Sci, Inst Math & Stat, BR-05508090 Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: BRIEFINGS IN BIOINFORMATICS; v. 15, n. 6, p. 906-918, NOV 2014.
• Citações Web of Science: 29

Resumo

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. (AU)

• Processo FAPESP: 13/03447-6 - Estruturas combinatórias, otimização e algoritmos em Teoria da Computação
• Beneficiário: Carlos Eduardo Ferreira
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 12/25417-9 - Desenvolvimento de métodos estatístico-computacionais para análise de grafos com aplicações em redes biológicas
• Beneficiário: Suzana de Siqueira Santos
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 11/07762-8 - Causalidade de Granger entre grupos de séries temporais: desenvolvimento de metodologias para seleção de modelos e extensões no domínio da frequência com aplicações em biologia molecular e neurociência
• Beneficiário: André Fujita
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático