CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra
Carregando...
Citações na Scopus
20
Tipo de produção
article
Data de publicação
2015
Título da Revista
ISSN da Revista
Título do Volume
Editora
PUBLIC LIBRARY SCIENCE
Autores
SANTOS, Suzana de Siqueira
FUJITA, Andre
Citação
PLOS ONE, v.10, n.8, article ID e0135831, 17p, 2015
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.
Palavras-chave
Referências
- Amar D, 2013, PLOS COMPUT BIOL, V9, DOI 10.1371/journal.pcbi.1002955
- Barabasi AL, 2004, NAT REV GENET, V5, P101, DOI 10.1038/nrg1272
- BENJAMINI Y, 1995, J ROY STAT SOC B MET, V57, P289
- Chan WY, 2000, AM J PATHOL, V156, P409, DOI 10.1016/S0002-9440(10)64744-X
- Choi Y, 2009, BIOINFORMATICS, V25, P2780, DOI 10.1093/bioinformatics/btp502
- Dai MH, 2005, NUCLEIC ACIDS RES, V33, DOI 10.1093/nar/gni179
- de la Fuente A, 2010, TRENDS GENET, V26, P326, DOI 10.1016/j.tig.2010.05.001
- Huber RM, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0057793
- Hudson NJ, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000382
- Irizarry RA, 2003, BIOSTATISTICS, V4, P249, DOI 10.1093/biostatistics/4.2.249
- Kato K, 2003, INT J GYNECOL PATHOL, V22, P334, DOI 10.1097/01.pgp.000092129.10100.5e
- Keller MP, 2008, GENOME RES, V18, P706, DOI 10.1101/gr.074914.107
- Kendall MG, 1938, BIOMETRIKA, V30, P81, DOI 10.2307/2332226
- Langfelder P, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-559
- Liu BH, 2010, BIOINFORMATICS, V26, P2637, DOI 10.1093/bioinformatics/btq471
- Pearson K, 1920, BIOMETRIKA, V13, P25, DOI 10.2307/2331722
- Purow BW, 2005, CANCER RES, V65, P2353, DOI 10.1158/0008-5472.CAN-04-1890
- Rahmatallah Y, 2014, BIOINFORMATICS, V30, P360, DOI 10.1093/bioinformatics/btt687
- Shannon P, 2003, GENOME RES, V13, P2498, DOI 10.1101/gr.1239303
- Silverman BW, 1986, DENSITY ESTIMATION S
- Spearman C, 1904, AM J PSYCHOL, V15, P72, DOI 10.2307/1412159
- Stockhausen MT, 2010, NEURO-ONCOLOGY, V12, P199, DOI 10.1093/neuonc/nop022
- Sturges HA, 1926, J AM STAT ASSOC, V21, P65
- Subramanian A, 2005, P NATL ACAD SCI USA, V102, P15545, DOI 10.1073/pnas.0506580102
- Takahashi DY, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0049949
- Tesson BM, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-497
- Van Mieghem P, 2010, GRAPH SPECTRA COMPLE
- Watson M, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-509
- Yang J, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0079729
- Yu H, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-315
- Zhang XH, 2012, CANCER SCI, V103, P181, DOI 10.1111/j.1349-7006.2011.02154.x