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Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier

Martin Lauss1, Attila Frigyesi2, Tobias Ryden3 and Mattias Höglund1*

Author Affiliations

1 Department of Oncology, Clinical Sciences, Lund University and Lund University Hospital, SE-221 85 LUND, Sweden

2 Department of Anesthesiology and Intensive Care, Lund University Hospital, SE-221 85 Lund, Sweden

3 Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden

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BMC Cancer 2010, 10:532  doi:10.1186/1471-2407-10-532

Published: 6 October 2010

Abstract

Background

Genome wide gene expression data is a rich source for the identification of gene signatures suitable for clinical purposes and a number of statistical algorithms have been described for both identification and evaluation of such signatures. Some employed algorithms are fairly complex and hence sensitive to over-fitting whereas others are more simple and straight forward. Here we present a new type of simple algorithm based on ROC analysis and the use of metagenes that we believe will be a good complement to existing algorithms.

Results

The basis for the proposed approach is the use of metagenes, instead of collections of individual genes, and a feature selection using AUC values obtained by ROC analysis. Each gene in a data set is assigned an AUC value relative to the tumor class under investigation and the genes are ranked according to these values. Metagenes are then formed by calculating the mean expression level for an increasing number of ranked genes, and the metagene expression value that optimally discriminates tumor classes in the training set is used for classification of new samples. The performance of the metagene is then evaluated using LOOCV and balanced accuracies.

Conclusions

We show that the simple uni-variate gene expression average algorithm performs as well as several alternative algorithms such as discriminant analysis and the more complex approaches such as SVM and neural networks. The R package rocc is freely available at http://cran.r-project.org/web/packages/rocc/index.html webcite.