Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data

Benchmarking RNA-seq differential expression analysis methods using spike-in and simulated RNA-seq data has often yielded inconsistent results. The spike-in data, which were generated from the same bulk RNA sample, only represent technical variability, making the test results less reliable. We compa...

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Bibliographic Details
Published in:PloS one 2020-04, Vol.15 (4), p.e0232271-e0232271
Main Authors: Baik, Bukyung, Yoon, Sora, Nam, Dougu
Format: Article
Language:English
Subjects:
Benchmarks
Biology and Life Sciences
Computer simulation
Datasets
Gene expression
Genes
Methods
Outliers (statistics)
Parameter estimation
Physical Sciences
Quality control
Research and Analysis Methods
Ribonucleic acid
RNA
Simulation
Spikes
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Summary:Benchmarking RNA-seq differential expression analysis methods using spike-in and simulated RNA-seq data has often yielded inconsistent results. The spike-in data, which were generated from the same bulk RNA sample, only represent technical variability, making the test results less reliable. We compared the performance of 12 differential expression analysis methods for RNA-seq data, including recent variants in widely used software packages, using both RNA spike-in and simulation data for negative binomial (NB) model. Performance of edgeR, DESeq2, and ROTS was particularly different between the two benchmark tests. Then, each method was tested under most extensive simulation conditions especially demonstrating the large impacts of proportion, dispersion, and balance of differentially expressed (DE) genes. DESeq2, a robust version of edgeR (edgeR.rb), voom with TMM normalization (voom.tmm) and sample weights (voom.sw) showed an overall good performance regardless of presence of outliers and proportion of DE genes. The performance of RNA-seq DE gene analysis methods substantially depended on the benchmark used. Based on the simulation results, suitable methods were suggested under various test conditions.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0232271