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Som-Based Class Discovery Exploring the ICA-Reduced Features of Microarray Expression Profiles
2004
Comparative and Functional Genomics
Gene expression datasets are large and complex, having many variables and unknown internal structure. We apply independent component analysis (ICA) to derive a less redundant representation of the expression data. The decomposition produces components with minimal statistical dependence and reveals biologically relevant information. Consequently, to the transformed data, we apply cluster analysis (an important and popular analysis tool for obtaining an initial understanding of the data, usually
doi:10.1002/cfg.444
pmid:18629176
pmcid:PMC2447468
fatcat:2fvwy3ybv5hxjkyxk5lpltxgse