Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization.

BMC Bioinformatics
Kin-On ChengAlan Wee-Chung Liew

Abstract

The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only 1. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis. We develop a novel and efficient biclustering algorithm which can be regarded as a greedy version of an existing algorithm known as pCluster algorithm. By relaxing the constraint in homogeneity, the proposed algorithm has polynomial-time complexity in the worst case instead of exponential-time complexity as in the pCluster algorithm. Experiments on artificial datasets verify that our algorithm can identify both additive-related and multiplicative-related biclu...Continue Reading

References

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Citations

Aug 11, 2012·PloS One·Jiyuan AnColleen C Nelson
Aug 14, 2013·PloS One·Hung-Chia ChenJames J Chen
Dec 3, 2015·BioData Mining·Haifa Ben Saber, Mourad Elloumi
Sep 12, 2015·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Danilo Horta, Ricardo J G B Campello
May 25, 2012·BMC Bioinformatics·Wassim AyadiJin-Kao Hao

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Methods Mentioned

BETA
ISA
histone acetylation

Software Mentioned

pCluster
xMotifs
Window XP
PA
OPSM
Matlab
C MEX

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