Bioinformatics Vol. 18 no. 12 2002
Pages 1600-1608
© 2002 Oxford University Press
Between-group analysis of microarray data
1 Department of Biochemistry, University College Cork,
Cork, Ireland
2 Laboratoire de Biométrie et Biologie Évolutive,
UMR CNRS No. 5558, Université Claude Bernard—Lyon 1, 43, blvd. du 11 Novembre 1918,
69622 Villeurbanne Cedex, France
Received on March 12, 2002
; revised on May 20, 2002
; accepted on May 24, 2002
Motivation: Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data.
Results: We illustrate the power of the method using two cancer data sets. In both cases, we can quickly and accurately classify test samples from any number of specified a priori groups and identify the genes which characterize these groups. We obtained very high rates of correct classification, as determined by jack-knife or validation experiments with training and test sets. The results are comparable to those from other methods in terms of accuracy but the power and flexibility of BGA make it an especially attractive method for the analysis of microarray cancer data.
Availability: The methods described are implemented in ADE-4 which runs under MacOS and Windows, and is freely available at http://pbil.univ-lyon1.fr/ADE-4/. All scripts are available on request.
Contact: A.Culhane{at}ucc.ie
Supplementary information: Supplementary figures and tables are available at http://bioinfo.ucc.ie/BGA/.
* To whom correspondence should be addressed.
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