Biclustering of Expression Microarray Data with Topic Models

This paper presents an approach to extract biclusters from expression micro array data using topic models - a class of probabilistic models which allow to detect interpretable groups of highly correlated genes and samples. Starting from a topic model learned from the expression matrix, some automatic rules to extract biclusters are presented, which overcome the drawbacks of previous approaches. The methodology has been positively tested with synthetic benchmarks, as well as with a real experiment involving two different species of grape plants (Vitis vinifera and Vitis riparia).