A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data.
BACKGROUND
Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem.


RESULTS
We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion. Using simulated data, we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using freely available tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturing biologically relevant structure.
Teschendorff
Andrew E
AE
Department of Oncology, Cancer Genomics Program, Hutchison-MRC Research Centre, University of Cambridge Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk
Wang
Yanzhong
Y
Barbosa-Morais
Nuno L
NL
Brenton
James D
JD
Caldas
Carlos
C
D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D018401:Sample Size; D012984:Software