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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
Published on: March 15, 2011
Significance of gene ranking for classification of microarray samples
Chaolin Zhang1, Xuesong Lu, Xuegong Zhang
1Cold Spring Harbor Laboratory and the Department of Biomedical Engineering, State University of New York at Stony Brook, NY 11794, USA. zhangc@cshl.edu
IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 20, 2006
Summary
This study introduces a novel resampling-based method to evaluate the statistical significance of gene rankings from machine learning in microarray analysis. The proposed r-test provides position p-values, aiding gene selection and biological interpretation.
Area of Science:
- Bioinformatics
- Computational Biology
- Statistical Genetics
Background:
- Microarray data analysis commonly employs classification and gene selection methods, yielding gene rankings.
- Assessing the statistical significance of these gene rankings is crucial for biological insights but is often overlooked in machine learning approaches.
- Existing methods lack robust statistical frameworks for evaluating gene ranking significance.
Purpose of the Study:
- To develop a statistically rigorous method for evaluating the significance of gene rankings derived from machine learning algorithms applied to microarray data.
- To introduce a hypothesis testing framework that converts gene rankings into interpretable position p-values.
- To provide a tool for determining the optimal number of genes for selection and facilitate sophisticated downstream analysis.
Main Methods:
- Formulation of gene ranking evaluation within a hypothesis testing framework.
- Development of resampling-based methods (r-tests) to generate position p-values.
- Application and testing of the r-test methods on real and simulated microarray datasets using support vector machines for classification and gene selection.
Main Results:
- The proposed r-test methods successfully convert gene ranking results into position p-values, quantifying statistical significance.
- Position p-values effectively aid in determining the appropriate number of genes to select from microarray data.
- The methods allow for the analysis of gene selection results within a consistent statistical inference paradigm.
Conclusions:
- The developed r-test offers a statistically sound approach to assess gene ranking significance in machine learning-based microarray analysis.
- Position p-values provide a valuable metric for gene selection and enable advanced multivariate analysis of results.
- This work bridges the gap in statistical evaluation for machine learning gene selection methods, enhancing biological interpretability.
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