A temporal precedence based clustering method for gene expression microarray data.

BMC Bioinformatics
Ritesh KrishnaVicky Buchanan-Wollaston

Abstract

Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not. A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We ...Continue Reading

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Citations

Oct 31, 2012·BMC Systems Biology·André FujitaSatoru Miyano
Aug 2, 2014·International Journal of Neural Systems·Camelia ChiraEmilio Corchado

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Software Mentioned

Cytoscape
Matlab
Imagene
BinGO

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