Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices.
Biclustering algorithms represent a key methodological advance in analysing gene expression data, enabling simultaneous clustering of both genes and experimental conditions. This dual clustering ...
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