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A novel estimator of between-study variance in random-effects models
1School of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China.
BMC Genomics
|February 13, 2020
Summary
A new meta-analysis method, DSLD2, effectively identifies differentially expressed genes by estimating between-study variance. Applied to Alzheimer's data, it found significant enrichment in neurological diseases.
Area of Science:
- Genomics
- Bioinformatics
- Statistical genetics
Background:
- High-throughput sequencing generates numerous biological datasets.
- Meta-analysis integrates results from multiple studies on the same topic.
- Random-effects models account for between-study variance in meta-analysis.
Purpose of the Study:
- To propose a novel moments estimator for between-study variance.
- To introduce a new two-step random-effects meta-analysis method, DSLD2.
- To evaluate DSLD2's performance in identifying differentially expressed genes.
Main Methods:
- Developed a moments estimator for across-study variation.
- Introduced the DSLD2 method with a two-step estimation process.
- Compared DSLD2 with six other meta-analysis methods using effect sizes across three hypothesis settings.
Main Results:
- DSLD2 demonstrated suitability for identifying differentially expressed genes under the first hypothesis.
- Application to Alzheimer's microarray data revealed significant enrichment of detected genes in neurological diseases.
Conclusions:
- Both simulations and real-world data application confirm DSLD2's efficacy.
- DSLD2 is a suitable method for detecting differentially expressed genes under specific hypotheses.
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