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A disattenuated correlation estimate when variables are measured with error: illustration estimating cross-platform
K J Archer1, C I Dumur, G S Taylor
1Department of Biostatistics, Virginia Commonwealth University, 1101 East Marshall Street, Richmond, VA 23298, U.S.A. kjarcher@vcu.edu
Statistics in Medicine
|June 30, 2007
Summary
This study introduces measurement error models to estimate gene-specific correlations, improving clone prioritization for sequence verification. The novel
Area of Science:
- Biostatistics
- Genomics
- Bioinformatics
Background:
- Cross-platform reproducibility studies commonly use Pearson's correlation to assess gene expression consistency.
- Existing methods may not adequately account for measurement error inherent in different platforms.
- Reliability in gene expression data is crucial for accurate analysis and clone selection.
Purpose of the Study:
- To propose measurement error models for estimating gene-specific correlations in cross-platform studies.
- To demonstrate the utility of gene-specific reliability estimates for prioritizing clones.
- To introduce a 'disattenuated' correlation method for studies with measurement error.
Main Methods:
- Application of measurement error models to estimate gene-specific correlations.
- Development of gene-specific reliability estimates.
- Comparison of proposed method with traditional Pearson's correlation coefficient.
Main Results:
- Measurement error models provide a more accurate estimation of gene-specific correlations.
- Gene-specific reliability estimates effectively prioritize clones for sequence verification.
- The 'disattenuated' correlation offers a robust measure when both variables are subject to error.
Conclusions:
- Measurement error models offer a superior approach to assessing cross-platform reproducibility in gene expression studies.
- Gene-specific reliability is a valuable metric for clone selection, outperforming random sampling.
- The proposed 'disattenuated' correlation is applicable to various scientific fields involving measurements with error.
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