Open AccessSOM‐based class discovery exploring the ICA‐reduced features of microarray expression profiles
Author(s) -
Dragomir Andrei,
Mavroudi Seferina,
Bezerianos Anastasios
Publication year - 2004
Publication title -
comparative and functional genomics
Language(s) - English
Resource type - Journals
eISSN - 1532-6268
pISSN - 1531-6912
DOI - 10.1002/cfg.444
Subject(s) - cluster analysis , computer science , data mining , independent component analysis , pattern recognition (psychology) , self organizing map , artificial intelligence , principal component analysis , class (philosophy) , knowledge extraction , representation (politics) , entropy (arrow of time) , expression (computer science) , machine learning , physics , quantum mechanics , politics , political science , law , programming language
Abstract 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 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. Copyright © 2005 John Wiley & Sons, Ltd.