Cargando…
Modeling measurement error in tumor characterization studies
BACKGROUND: 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...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3213130/ https://www.ncbi.nlm.nih.gov/pubmed/21752297 http://dx.doi.org/10.1186/1471-2105-12-284 |
_version_ | 1782216085235302400 |
---|---|
author | Rakovski, Cyril Weisenberger, Daniel J Marjoram, Paul Laird, Peter W Siegmund, Kimberly D |
author_facet | Rakovski, Cyril Weisenberger, Daniel J Marjoram, Paul Laird, Peter W Siegmund, Kimberly D |
author_sort | Rakovski, Cyril |
collection | PubMed |
description | BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-3213130 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32131302011-11-11 Modeling measurement error in tumor characterization studies Rakovski, Cyril Weisenberger, Daniel J Marjoram, Paul Laird, Peter W Siegmund, Kimberly D BMC Bioinformatics Methodology Article BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. BioMed Central 2011-07-13 /pmc/articles/PMC3213130/ /pubmed/21752297 http://dx.doi.org/10.1186/1471-2105-12-284 Text en Copyright ©2011 Rakovski et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Rakovski, Cyril Weisenberger, Daniel J Marjoram, Paul Laird, Peter W Siegmund, Kimberly D Modeling measurement error in tumor characterization studies |
title | Modeling measurement error in tumor characterization studies |
title_full | Modeling measurement error in tumor characterization studies |
title_fullStr | Modeling measurement error in tumor characterization studies |
title_full_unstemmed | Modeling measurement error in tumor characterization studies |
title_short | Modeling measurement error in tumor characterization studies |
title_sort | modeling measurement error in tumor characterization studies |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3213130/ https://www.ncbi.nlm.nih.gov/pubmed/21752297 http://dx.doi.org/10.1186/1471-2105-12-284 |
work_keys_str_mv | AT rakovskicyril modelingmeasurementerrorintumorcharacterizationstudies AT weisenbergerdanielj modelingmeasurementerrorintumorcharacterizationstudies AT marjorampaul modelingmeasurementerrorintumorcharacterizationstudies AT lairdpeterw modelingmeasurementerrorintumorcharacterizationstudies AT siegmundkimberlyd modelingmeasurementerrorintumorcharacterizationstudies |