Evaluating statistical methods using plasmode data sets in the age of massive public databases: an illustration using
Gary L Gadbury1, Qinfang Xiang, Lin Yang
1Department of Statistics, Kansas State University, Manhattan, Kansas, United States of America.
Plos Genetics
|June 21, 2008
Summary
This study introduces plasmode data for realistic statistical method evaluation. Using real omics data, it compares 15 methods for high-dimensional experiments, improving false discovery rate estimation.
Area of Science:
- Biostatistics
- Genomics
- Computational Biology
Background:
- Omic techniques generate large datasets, increasing data sharing.
- Real data with known truth (plasmodes) are crucial for empirical statistical validation.
- Current methods for high-dimensional experiments often use unrealistic simulations.
Purpose of the Study:
- To introduce and utilize plasmode data for empirical statistical method evaluation.
- To compare the performance of 15 statistical methods in high-dimensional experiments.
- To assess estimation of true null hypotheses and false discovery rates using realistic data.
Main Methods:
- Developed a simulation procedure using actual high-dimensional experimental data with known parameters.
- Created plasmode datasets from real omics data.
- Applied 15 different statistical methods to these plasmode datasets.
Main Results:
- Demonstrated the utility of plasmode data for empirical statistical analysis.
- Compared estimates of the proportion of true null hypotheses and false discovery rates (FDR).
- Evaluated local FDR estimates from 15 distinct statistical methods.
Conclusions:
- Plasmode data enables realistic empirical validation of statistical methods for high-dimensional data.
- The study provides a benchmark for comparing multiple testing procedures.
- This approach enhances the reliability of statistical findings in omics research.
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