Supervised cluster analysis for microarray data based on multivariate Gaussian mixture.
BACKGROUND
Grouping genes having similar expression patterns is called gene clustering, which has been proved to be a useful tool for extracting underlying biological information of gene expression data. Many clustering procedures have shown success in microarray gene clustering; most of them belong to the family of heuristic clustering algorithms. Model-based algorithms are alternative clustering algorithms, which are based on the assumption that the whole set of microarray data is a finite mixture of a certain type of distributions with different parameters. Application of the model-based algorithms to unsupervised clustering has been reported. Here, for the first time, we demonstrated the use of the model-based algorithm in supervised clustering of microarray data.


RESULTS
We applied the proposed methods to real gene expression data and simulated data. We showed that the supervised model-based algorithm is superior over the unsupervised method and the support vector machines (SVM) method.


BACKGROUND
The program written in the SAS language implementing methods I-III in this report is available upon request. The software of SVMs is available in the website http://svm.sdsc.edu/cgi-bin/nph-SVMsubmit.cgi
Qu
Yi
Y
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.
Xu
Shizhong
S
D000465:Algorithms; D001185:Artificial Intelligence; D018797:Cell Cycle Proteins / Q000235:genetics; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D015999:Multivariate Analysis; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
D018797:Cell Cycle Proteins; D029701:Saccharomyces cerevisiae Proteins