Functional clustering of time series gene expression data by Granger causality

André Fujita, Patricia Severino, Kaname Kojima, João R. Sato, Alexandre G. Patriota, Satoru Miyano

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Background: 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.Results: 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.Conclusions: 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.

Original languageEnglish
Article number137
JournalBMC Systems Biology
Volume6
DOIs
Publication statusPublished - 2012 Oct 30

ASJC Scopus subject areas

  • Structural Biology
  • Modelling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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