Description:

Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C++ based on Rcpp package.

Changes to previous version:

New option nL: maximal number of biclusters per row element; Sort biclusters according to information content; Improved and extended preprocessing; Update to R2.13

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Any Format Supported By R

Tags: Bioinformatics, Clustering, Bioconductor, Matrix Factorization, Sparse Learning, Variational Inference, Biclustering, Gene Expression

Archive: download here

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