Abstract
BackgroundA 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.ResultsIn 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.ConclusionsThis 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.
Highlights
A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time
Simulated data In order to study the properties of the proposed functional clustering method and to check its consistency, we performed four simulations with distinct network characteristics in terms of structure and Granger causality
The same result was obtained with varying numbers of sub-networks or when Granger causality within clusters increased, demonstrating the consistency of the method
Summary
A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Gene network analysis of complex datasets, such as DNA microarray results, aims to identify relevant structures that help the understanding of a certain phenotype or condition. These networks comprise hundreds to thousands of genes that may interact generating intricate structures. Common analyses explore gene-gene level relationships and generate broad networks. This is a valuable approach, genes might interact more intensely to a few members of the network, and the identification of these so-called sub-networks should lead to a better comprehension of the entire regulatory process.
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