Som-Based Class Discovery Exploring the ICA-Reduced Features of Microarray Expression Profiles

Andrei Dragomir, Seferina Mavroudi, Anastasios Bezerianos
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
more » ... employed for class discovery). The proposed self-organizing map (SOM)-based clustering algorithm automatically determines the number of 'natural' subgroups of the data, being aided at this task by the available prior knowledge of the functional categories of genes. An entropy criterion allows each gene to be assigned to multiple classes, which is closer to the biological representation. These features, however, are not achieved at the cost of the simplicity of the algorithm, since the map grows on a simple grid structure and the learning algorithm remains equal to Kohonen's one.
doi:10.1002/cfg.444 pmid:18629176 pmcid:PMC2447468 fatcat:2fvwy3ybv5hxjkyxk5lpltxgse