Bioinformatics Vol. 19 no. 5 2003
Pages 571-578
© 2003 Oxford University Press
PCA disjoint models for multiclass cancer analysis using gene expression data
Department of Chemical Process Engineering, University of Padova, via Marzolo, 9, 35131, Padova, Italy
Received on May 27, 2002
; revised on October 7, 2002
; accepted on October 30, 2002
Motivation: Microarray expression profiling appears particularly promising for a deeper understanding of cancer biology and to identify molecular signatures supporting the histological classification schemes of neoplastic specimens. However, molecular diagnostics based on microarray data presents major challenges due to the overwhelming number of variables and the complex, multiclass nature of tumor samples. Thus, the development of marker selection methods, that allow the identification of those genes that are most likely to confer high classification accuracy of multiple tumor types, and of multiclass classification schemes is of paramount importance.
Results: A computational procedure for marker identification and for classification of multiclass gene expression data through the application of disjoint principal component models is described. The identified features represent a rational and dimensionally reduced base for understanding the basic biology of diseases, defining targets for therapeutic intervention, and developing diagnostic tools for the identification and classification of multiple pathological states. The method has been tested on different microarray data sets obtained from various human tumor samples. The results demonstrate that this procedure allows the identification of specific phenotype markers and can classify previously unseen instances in the presence of multiple classes.
Availability: Matlab source codes are available from the authors.
Contact: silvio.bicciato{at}unipd.it
Supplementary information: http://www.dpci.unipd.it/PersPages/SBicciato/SIMCArray.html
* To whom correspondence should be addressed.
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