Two-way clustering of gene expression profiles by sparse matrix factorization
2005
We propose a new methodology for two-way cluster analysis of gene expression data using a novel sparse matrix factorization technique that produces a decomposition of a matrix in a set of sparse factors. This method produces a set of bases and coding matrices that are not only able to represent the original data, but they also extract important localized parts-based patterns. We applied the method to gene expression data sets in an attempt to uncover latent relationships between samples and genes in DNA microarray experiments.
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