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A bayesian analysis strategy for cross-study translation of gene expression biomarkers.

Publication ,  Journal Article
Lucas, J; Carvalho, C; West, M
Published in: Statistical applications in genetics and molecular biology
January 2009

We describe a strategy for the analysis of experimentally derived gene expression signatures and their translation to human observational data. Sparse multivariate regression models are used to identify expression signature gene sets representing downstream biological pathway events following interventions in designed experiments. When translated into in vivo human observational data, analysis using sparse latent factor models can yield multiple quantitative factors characterizing expression patterns that are often more complex than in the controlled, in vitro setting. The estimation of common patterns in expression that reflect all aspects of covariation evident in vivo offers an enhanced, modular view of the complexity of biological associations of signature genes. This can identify substructure in the biological process under experimental investigation and improved biomarkers of clinical outcomes. We illustrate the approach in a detailed study from an oncogene intervention experiment where in vivo factor profiling of an in vitro signature generates biological insights related to underlying pathway activities and chromosomal structure, and leads to refinements of cancer recurrence risk stratification across several cancer studies.

Duke Scholars

Published In

Statistical applications in genetics and molecular biology

DOI

EISSN

1544-6115

ISSN

2194-6302

Publication Date

January 2009

Volume

8

Start / End Page

Article / 11

Related Subject Headings

  • Vinculin
  • Survival Analysis
  • Regression Analysis
  • Proto-Oncogene Proteins c-myc
  • Paxillin
  • Normal Distribution
  • Models, Genetic
  • Humans
  • Genome-Wide Association Study
  • Gene Expression Profiling
 

Citation

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Lucas, J., Carvalho, C., & West, M. (2009). A bayesian analysis strategy for cross-study translation of gene expression biomarkers. Statistical Applications in Genetics and Molecular Biology, 8, Article-11. https://doi.org/10.2202/1544-6115.1436
Lucas, Joseph, Carlos Carvalho, and Mike West. “A bayesian analysis strategy for cross-study translation of gene expression biomarkers.Statistical Applications in Genetics and Molecular Biology 8 (January 2009): Article-11. https://doi.org/10.2202/1544-6115.1436.
Lucas J, Carvalho C, West M. A bayesian analysis strategy for cross-study translation of gene expression biomarkers. Statistical applications in genetics and molecular biology. 2009 Jan;8:Article-11.
Lucas, Joseph, et al. “A bayesian analysis strategy for cross-study translation of gene expression biomarkers.Statistical Applications in Genetics and Molecular Biology, vol. 8, Jan. 2009, p. Article-11. Epmc, doi:10.2202/1544-6115.1436.
Lucas J, Carvalho C, West M. A bayesian analysis strategy for cross-study translation of gene expression biomarkers. Statistical applications in genetics and molecular biology. 2009 Jan;8:Article-11.
Journal cover image

Published In

Statistical applications in genetics and molecular biology

DOI

EISSN

1544-6115

ISSN

2194-6302

Publication Date

January 2009

Volume

8

Start / End Page

Article / 11

Related Subject Headings

  • Vinculin
  • Survival Analysis
  • Regression Analysis
  • Proto-Oncogene Proteins c-myc
  • Paxillin
  • Normal Distribution
  • Models, Genetic
  • Humans
  • Genome-Wide Association Study
  • Gene Expression Profiling