Teschendorff, AE and Wang, YZ and Barbosa-Morais, NL and Brenton, JD and Caldas, C (2005) A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data. BIOINFORMATICS , 21 (13) 3025 - 3033. 10.1093/bioinformatics/bti466.
Full text not available from this repository.
Motivation: 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.
|Title:||A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data|
|Keywords:||MICROARRAY DATA, CLASS DISCOVERY, BREAST-CANCER, CLASSIFICATION, PATTERNS|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Medical Sciences > Wolfson Institute and Cancer Institute Administration > Cancer Institute > Research Department of Cancer Biology|
Archive Staff Only: edit this record