Biologically supervised hierarchical clustering algorithms for gene expression data.
Cluster analysis has become a standard part of gene expression analysis. In this paper, we propose a novel semi-supervised approach that offers the same flexibility as that of a hierarchical clustering. Yet it utilizes, along with the experimental gene expression data, common biological information about different genes that is being complied at various public, Web accessible databases. We argue that such an approach is inherently superior than the standard unsupervised approach of grouping genes based on expression data alone. It is shown that our biologically supervised methods produce better clustering results than the corresponding unsupervised methods as judged by the distance from the model temporal profiles. R-codes of the clustering algorithm are available from the authors upon request.
Boratyn
Grzegorz M
GM
Kidney Disease program and Clinical Proteomics Center, University of Louisville, KY, USA. greg.boratyn@louisville.edu
Datta
Susmita
S
Datta
Somnath
S
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000295:instrumentation* / Q000379:methods*; D005786:Gene Expression Regulation; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D017422:Sequence Analysis, DNA