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.
|Author||Sepp Hochreiter <[email protected]>|
|Bioconductor views||Clustering DifferentialExpression Microarray MultipleComparison StatisticalMethod Visualization|
|Maintainer||Sepp Hochreiter <[email protected]>|
|License||LGPL (>= 2.1)|
|Package repository||View on GitHub|
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