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Comparative analysis of differential gene expression tools for RNA sequencing time course data
Daniel Spies1,2, Peter F Renz1,2, Tobias A Beyer1
1Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.
Briefings in Bioinformatics
|October 14, 2017
Summary
RNA sequencing time course analysis is improving, but pairwise comparisons often outperform specialized tools for short series. Combining results from multiple tools can enhance accuracy by reducing false positives.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- RNA sequencing (RNA-seq) is crucial for studying gene expression changes.
- Differential expression (DE) analysis for RNA-seq time course (TC) data is less developed than pairwise comparisons.
- Existing TC RNA-seq tools require thorough evaluation.
Purpose of the Study:
- To comprehensively compare existing RNA-seq TC DE analysis tools.
- To evaluate tool performance on simulated and real-world data.
- To identify optimal methods for TC RNA-seq data analysis.
Main Methods:
- Extensive simulation of RNA-seq TC data.
- Performance evaluation of multiple TC RNA-seq DE analysis algorithms.
- Validation of top-performing tools on published datasets.
Main Results:
- Pairwise comparison outperformed most TC tools on short time series (<8 points) due to high false positives, except for ImpulseDE2.
- Combining candidate lists from different tools reduced false positives without significantly impacting true positives.
- For longer time series, splineTC and maSigPro were more efficient and identified no false positives, outperforming the pairwise approach.
Conclusions:
- Classical pairwise DE analysis remains competitive for short RNA-seq time series, despite limitations.
- Consensus-based approaches by overlapping gene lists can improve the reliability of TC DE analysis.
- Advanced tools like splineTC and maSigPro show promise for longer time series analysis, offering high accuracy and specificity.