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Stability of methods for differential expression analysis of RNA-seq data
1Institute of Statistical Sciences, College of Mathematics and Statistics, Shenzhen University, Shenzhen, China.
BMC Genomics
|January 13, 2019
Summary
Researchers often overlook differential expression (DE) method stability. This study introduces AUCOR, a novel metric to assess DE method stability in RNA-sequencing data, revealing factors influencing it.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- RNA-sequencing (RNA-seq) is a key method for measuring gene expression.
- Differential expression (DE) analysis is crucial for RNA-seq data interpretation.
- The stability of DE methods has been largely overlooked in favor of validity.
Purpose of the Study:
- To highlight the importance of assessing DE method stability.
- To propose a novel metric for evaluating DE method stability.
- To investigate factors affecting DE method stability.
Main Methods:
- Developed a stability metric named Area Under the Correlation curve (AUCOR).
- AUCOR generates perturbed datasets using a mixture distribution.
- It assesses the similarity between feature sets from perturbed and original datasets.
Main Results:
- Empirically demonstrated the necessity of evaluating DE method stability.
- AUCOR effectively ranks DE methods based on their stability for RNA-seq datasets.
- Identified biological and technical factors influencing DE method stability.
Conclusions:
- AUCOR provides a reliable way to assess DE method stability.
- The study offers insights into experimental and analytical factors impacting stability.
- AUCOR is available as an open-source R package for community use.
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