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Open Access Research article

Quantifying stability in gene list ranking across microarray derived clinical biomarkers

Sebastian Schneckener1, Nilou S Arden234 and Andreas Schuppert12*

Author Affiliations

1 Bayer AG, Bayer Technology Services, 51368 Leverkusen, Germany

2 Aachen Institute for Computational Engineering Sciences, RWTH, Schinkelstrasse 2, Rogowski Building, 52062 Aachen, Germany

3 The Johns Hopkins University, Department of Applied and Computational Mathematics, Applied Physics Laboratory, Laurel, MD 20723, USA

4 NovoCatalysis LLC, Palo Alto, CA 94306, USA

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BMC Medical Genomics 2011, 4:73  doi:10.1186/1755-8794-4-73

Published: 14 October 2011

Abstract

Background

Identifying stable gene lists for diagnosis, prognosis prediction, and treatment guidance of tumors remains a major challenge in cancer research. Microarrays measuring differential gene expression are widely used and should be versatile predictors of disease and other phenotypic data. However, gene expression profile studies and predictive biomarkers are often of low power, requiring numerous samples for a sound statistic, or vary between studies. Given the inconsistency of results across similar studies, methods that identify robust biomarkers from microarray data are needed to relay true biological information. Here we present a method to demonstrate that gene list stability and predictive power depends not only on the size of studies, but also on the clinical phenotype.

Results

Our method projects genomic tumor expression data to a lower dimensional space representing the main variation in the data. Some information regarding the phenotype resides in this low dimensional space, while some information resides in the residuum. We then introduce an information ratio (IR) as a metric defined by the partition between projected and residual space. Upon grouping phenotypes such as tumor tissue, histological grades, relapse, or aging, we show that higher IR values correlated with phenotypes that yield less robust biomarkers whereas lower IR values showed higher transferability across studies. Our results indicate that the IR is correlated with predictive accuracy. When tested across different published datasets, the IR can identify information-rich data characterizing clinical phenotypes and stable biomarkers.

Conclusions

The IR presents a quantitative metric to estimate the information content of gene expression data with respect to particular phenotypes.