Biclustering by Sparse Factor Analysis: FABIA

Description

Biclustering of gene expression data, that is, clustering genes and samples simultaneously, is an important unsupervised approach to analyze transcriptomic data. We introduce a novel generative model for biclustering called ``Factor Analysis for Bicluster Acquisition'' (FABIA). FABIA is exploratory factor analysis where both the factors and the loadings are sparse, that is they contain many zeros. For each factor, the posterior factor values and the factor's loading vector determine the membership of samples and genes, respectively, to the bicluster associated with this factor. The degree of sparseness governs the size of the biclusters. We report how FABIA performs on different gene expression data sets

Period	14 Sept 2010
Event title	ENBIS European Network for Business and Industrial Statistics
Event type	Conference
Location	BelgiumShow on map