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Functional clustering of time series gene expression data by Granger causality

André Fujita1*, Patricia Severino2, Kaname Kojima3, João Ricardo Sato4, Alexandre Galvão Patriota1 and Satoru Miyano3

Author affiliations

1 Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010, São Paulo 05508-090, Brazil

2 Center for Experimental Research, Albert Einstein Research and Education Institute, Av. Albert Einstein, 627 - São Paulo, 05652-000, Brazil

3 Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan

4 Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Rua santa Adélia, 166 - Santo André, 09210-170, Brazil

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Citation and License

BMC Systems Biology 2012, 6:137  doi:10.1186/1752-0509-6-137

Published: 30 October 2012



A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.


In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence.


This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.