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Microarrays, pattern recognition and exploratory data analysis
Author(s) -
Mertens Bart J. A.
Publication year - 2003
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1364
Subject(s) - computer science , exploratory data analysis , exploratory analysis , pattern recognition (psychology) , data mining , artificial intelligence , data science
We explore the application of principal component based approaches to pattern recognition in microarray analysis. A comparative assessment is presented, based on predictive evaluation, following a review of some key methodology. On the basis of these results, we select principal component based linear discrimination for a further in‐depth analysis. We are particularly interested in studying the use of principal component decomposition for the evaluation of the condition of estimation of the pooled covariance matrix. The results are used to give some guidance as to how principal components may be used as a data exploratory tool in the analysis of microarray data. Opportunities for further development are outlined as well as implications for wider statistical modelling of microarray data. Copyright © 2003 John Wiley & Sons, Ltd.