Network constrained clustering for gene microarray data.
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.


BACKGROUND
The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://www.cran.org.
Zhu
Dongxiao
D
Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu
Hero
Alfred O
AO
Cheng
Hong
H
Khanna
Ritu
R
Swaroop
Anand
A
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*; D015533:Transcriptional Activation / Q000502:physiology
D020543:Proteome; D014157:Transcription Factors