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Bioinformatics Advance Access published online on December 7, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti197
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Bioinformatics © Oxford University Press 2004; all rights reserved.
Received July 28, 2004
Revised October 12, 2004
Accepted November 29, 2004

Article

Characterizing the dynamic connectivity between genes by variable parameter regression and Kalman filtering based on temporal gene expression data

Qinghua Cui 1, Bing Liu 1, Tianzi Jiang 1*, and Songde Ma 1

1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, P. R. China

* To whom correspondence should be addressed.
Tianzi Jiang, E-mail: jiangtz{at}nlpr.ia.ac.cn


  Abstract

Motivation: one popular method for analyzing functional connectivity between genes is to cluster genes with similar expression profiles. The most popular metrics measuring the similarity (or dissimilarity) among genes include Pearson's correlation, linear regression coefficient and Euclidean distance, etc. Because these metrics only give some constant values, they can only depict a stationary connectivity between genes. However, the functional connectivity between genes usually changes along time. Here we introduce a novel insight for characterizing the relationship between genes and find out a proper mathematical model, variable parameter regression and Kalman filtering, to model it.

Results: We applied our algorithm to some simulated data and two pairs of real gene expression data. The changes of connectivity in simulated data are closely identical with the truth, and the results of two pairs of gene expression data show that our method has demonstrated successfully the dynamic connectivity between genes.


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[Abstract] [Full Text] [PDF]


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