Penalized factor mixture analysis for variable selection in clustered data
Computational Statistics & Data Analysis
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing
The Journal of Machine Learning Research
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
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Summary: One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail owing to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on. Availability: The ArrayCluster can be used free of charge for non-commercial and academic use and downloaded from http://www.ism.ac.jp/~higuchi/arraycluster.htm Contact: yoshidar@ims.u-tokyo.ac.jp