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Bioinformatics Advance Access published online on May 8, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl174
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received April 1, 2006
Revised April 25, 2006
Accepted April 30, 2006

Article

PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer

Andrew E. Teschendorff 1 *, Ali Naderi 1, Nuno L. Barbosa-Morais 2, and Carlos Caldas 1

1 Cancer Genomics Program, Department of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Hills Road, Cambridge CB2 2XZ, UK
2 Cancer Genomics Program, Department of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Hills Road, Cambridge CB2 2XZ, UK; Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal

* To whom correspondence should be addressed.
Andrew E. Teschendorff, E-mail: aet21{at}cam.ac.uk


  Abstract

Motivation: Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable.

Results: We present a novel feature selection procedure based on a mixture model and a non-gaussianity measure of a gene's expression profile. The method can be used to find genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. We validate our methodology using six independent data sets by rediscovering major classifiers in ER negative and ER positive breast cancer and in prostate cancer. Furthermore, our method finds two novel subtypes within the basal subgroup of ER negative breast tumours, associated with apoptotic and immune response functions respectively, and with statistically different clinical outcome.

Availability: An R-function pack that implements the methods used here has been added to vabayelMix, available from (www.cran.r-project.org).

Associate Editor: Martin Bishop
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