Introduction

UNLABELLED: Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways from gene microarray data. Based on the construction of co-expression networks that consists of both significantly linear and non-linear gene associations together with controlled biological and statistical significance, our approach tends to group functionally related genes into tight clusters despite their expression dissimilarities. We illustrate our approach and compare it to the traditional clustering approaches on a yeast galactose metabolism dataset and a retinal gene expression dataset. Our approach greatly outperforms the traditional approach in rediscovering the relatively well known galactose metabolism pathway in yeast and in clustering genes of the photoreceptor differentiation pathway. AVAILABILITY: The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://www.cran.org.

Publications

  1. Network constrained clustering for gene microarray data.
    Cite this
    Zhu D, Hero AO, Cheng H, Khanna R, Swaroop A, 2005-11-01 - Bioinformatics (Oxford, England)

Credits

  1. Dongxiao Zhu
    Developer

  2. Alfred O Hero
    Developer

  3. Hong Cheng
    Developer

  4. Ritu Khanna
    Developer

  5. Anand Swaroop
    Investigator

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Summary
AccessionBT000406
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByAnand Swaroop