Journal Article

Gene ranking and biomarker discovery under correlation

Verena Zuber and Korbinian Strimmer

in Bioinformatics

Volume 25, issue 20, pages 2700-2707
Published in print October 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI:
Gene ranking and biomarker discovery under correlation

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Motivation: Biomarker discovery and gene ranking is a standard task in genomic high-throughput analysis. Typically, the ordering of markers is based on a stabilized variant of the t-score, such as the moderated t or the SAM statistic. However, these procedures ignore gene–gene correlations, which may have a profound impact on the gene orderings and on the power of the subsequent tests.

Results: We propose a simple procedure that adjusts gene-wise t-statistics to take account of correlations among genes. The resulting correlation-adjusted t-scores (‘cat’ scores) are derived from a predictive perspective, i.e. as a score for variable selection to discriminate group membership in two-class linear discriminant analysis. In the absence of correlation the cat score reduces to the standard t-score. Moreover, using the cat score it is straightforward to evaluate groups of features (i.e. gene sets). For computation of the cat score from small sample data, we propose a shrinkage procedure. In a comparative study comprising six different synthetic and empirical correlation structures, we show that the cat score improves estimation of gene orderings and leads to higher power for fixed true discovery rate, and vice versa. Finally, we also illustrate the cat score by analyzing metabolomic data.

Availability: The shrinkage cat score is implemented in the R package ‘st’, which is freely available under the terms of the GNU General Public License (version 3 or later) from CRAN (


Journal Article.  5033 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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