| Full text | |
| Author(s): |
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]
Total Authors: 6
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Stat, Dept Comp Sci, Sao Paulo - Brazil
[2] Univ Sao Paulo, Sch Med, Dept Neurol, Sao Paulo - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | PLoS One; v. 10, n. 8 AUG 27 2015. |
| Web of Science Citations: | 7 |
| Abstract | |
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) | |
| FAPESP's process: | 14/09576-5 - Development of computational-statistical methods to construct, model and analyze biological networks associated with human diseases |
| Grantee: | André Fujita |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 12/25417-9 - Development of statistical and computational methods for the analysis of graphs with applications in biological networks |
| Grantee: | Suzana de Siqueira Santos |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 11/50761-2 - Models and methods of e-Science for life and agricultural sciences |
| Grantee: | Roberto Marcondes Cesar Junior |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 13/03447-6 - Combinatorial structures, optimization, and algorithms in theoretical Computer Science |
| Grantee: | Carlos Eduardo Ferreira |
| Support Opportunities: | Research Projects - Thematic Grants |