| Texto completo | |
| Autor(es): |
Santos, Suzana de Siqueira
[1]
;
de Almeida Galatro, Thais Fernanda
[2]
;
Watanabe, Rodrigo Akira
[2]
;
Oba-Shinjo, Sueli Mieko
[2]
;
Nagahashi Marie, Suely Kazue
[2]
;
Fujita, Andre
[1]
Número total de Autores: 6
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Inst Math & Stat, Dept Comp Sci, Sao Paulo - Brazil
[2] Univ Sao Paulo, Sch Med, Dept Neurol, Sao Paulo - Brazil
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PLoS One; v. 10, n. 8 AUG 27 2015. |
| Citações Web of Science: | 7 |
| Resumo | |
Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and enhancing statistical power over gene-by-gene analyses, it does not take into account the correlation (association) structure among the genes. In this work, we present CoGA (Co-expression Graph Analyzer), an R package for the identification of groups of differentially associated genes between two phenotypes. The analysis is based on concepts of Information Theory applied to the spectral distributions of the gene co-expression graphs, such as the spectral entropy to measure the randomness of a graph structure and the Jensen-Shannon divergence to discriminate classes of graphs. The package also includes common measures to compare gene co-expression networks in terms of their structural properties, such as centrality, degree distribution, shortest path length, and clustering coefficient. Besides the structural analyses, CoGA also includes graphical interfaces for visual inspection of the networks, ranking of genes according to their ``importance{''} in the network, and the standard differential expression analysis. We show by both simulation experiments and analyses of real data that the statistical tests performed by CoGA indeed control the rate of false positives and is able to identify differentially co-expressed genes that other methods failed. (AU) | |
| Processo FAPESP: | 14/09576-5 - Desenvolvimento de técnicas estatístico-computacionais para construir, modelar e analisar redes biológicas envolvidas em doenças humanas |
| Beneficiário: | André Fujita |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| 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: | 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias |
| Beneficiário: | Roberto Marcondes Cesar Junior |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| 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 |