Open Access Methodology article

Modeling measurement error in tumor characterization studies

Cyril Rakovski14, Daniel J Weisenberger23, Paul Marjoram1, Peter W Laird23 and Kimberly D Siegmund1*

Author Affiliations

1 Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, California 90089-9176, USA

2 Departments of Surgery and Biochemistry and Molecular Biology, USC Keck School of Medicine, Los Angeles, California 90089-9176, USA

3 USC Epigenome Center, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California 90089-9176, USA

4 Chapman University, Orange, California 92866, USA

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BMC Bioinformatics 2011, 12:284  doi:10.1186/1471-2105-12-284

Published: 13 July 2011



Etiologic studies of cancer increasingly use molecular features such as gene expression, DNA methylation and sequence mutation to subclassify the cancer type. In large population-based studies, the tumor tissues available for study are archival specimens that provide variable amounts of amplifiable DNA for molecular analysis. As molecular features measured from small amounts of tumor DNA are inherently noisy, we propose a novel approach to improve statistical efficiency when comparing groups of samples. We illustrate the phenomenon using the MethyLight technology, applying our proposed analysis to compare MLH1 DNA methylation levels in males and females studied in the Colon Cancer Family Registry.


We introduce two methods for computing empirical weights to model heteroscedasticity that is caused by sampling variable quantities of DNA for molecular analysis. In a simulation study, we show that using these weights in a linear regression model is more powerful for identifying differentially methylated loci than standard regression analysis. The increase in power depends on the underlying relationship between variation in outcome measure and input DNA quantity in the study samples.


Tumor characteristics measured from small amounts of tumor DNA are inherently noisy. We propose a statistical analysis that accounts for the measurement error due to sampling variation of the molecular feature and show how it can improve the power to detect differential characteristics between patient groups.