Research article

Microarray-based cancer prediction using single genes

Xiaosheng Wang and Richard Simon^*

• * Corresponding author: Richard Simon rsimon@mail.nih.gov

Author Affiliations

Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, MD 20852, USA

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BMC Bioinformatics 2011, 12:391 doi:10.1186/1471-2105-12-391

Published: 7 October 2011

Abstract

Background

Although numerous methods of using microarray data analysis for cancer classification have been proposed, most utilize many genes to achieve accurate classification. This can hamper interpretability of the models and ease of translation to other assay platforms. We explored the use of single genes to construct classification models. We first identified the genes with the most powerful univariate class discrimination ability and then constructed simple classification rules for class prediction using the single genes.

Results

We applied our model development algorithm to eleven cancer gene expression datasets and compared classification accuracy to that for standard methods including Diagonal Linear Discriminant Analysis, k-Nearest Neighbor, Support Vector Machine and Random Forest. The single gene classifiers provided classification accuracy comparable to or better than those obtained by existing methods in most cases. We analyzed the factors that determined when simple single gene classification is effective and when more complex modeling is warranted.

Conclusions

For most of the datasets examined, the single-gene classification methods appear to work as well as more standard methods, suggesting that simple models could perform well in microarray-based cancer prediction.