ArticleBioinformatics (Oxford, England)2021
Super-delta2: an enhanced differential expression analysis procedure for multi-group comparisons of RNA-seq data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- PXN Unlocks the Power of Public Gene Expression Data Through Cross-Technology Integration.bioRxiv : the preprint server for biology · 2026Article
- Tool choice matters: Evaluating edgeR vs. DESeq2 for sensitivity, robustness, and cross-study performance.PloS one · 2026Article
- FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.Bioinformatics (Oxford, England) · 2022Article
- Orthodenticle homeobox OTX1 is a potential prognostic biomarker for bladder cancer.Bioengineered · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
motivationWe developed super-delta2, a differential gene expression analysis pipeline designed for multi-group comparisons for RNA-seq data. It includes a customized one-way ANOVA F-test and a post-hoc test for pairwise group comparisons; both are designed to work with a multivariate normalization procedure to reduce technical noise. It also includes a trimming procedure with bias-correction to obtain robust and approximately unbiased summary statistics used in these tests. We demonstrated the asymptotic applicability of super-delta2 to log-transformed read counts in RNA-seq data by large sample theory based on Negative Binomial Poisson (NBP) distribution.
resultsWe compared super-delta2 with three commonly used RNA-seq data analysis methods: limma/voom, edgeR and DESeq2 using both simulated and real datasets. In all three simulation settings, super-delta2 not only achieved the best overall statistical power, but also was the only method that controlled type I error at the nominal level. When applied to a breast cancer dataset to identify differential expression pattern associated with multiple pathologic stages, super-delta2 selected more enriched pathways than other methods, which are directly linked to the underlying biological condition (breast cancer).
conclusionsIn conclusion, by incorporating trimming and bias-correction in the normalization step, super-delta2 was able to achieve tight control of type I error. Because the hypothesis tests are based on asymptotic normal approximation of the NBP distribution, super-delta2 does not require computationally expensive iterative optimization procedures used by methods such as edgeR and DESeq2, which occasionally have convergence issues. AVAILABILITY AND IMPLEMENTATION: Our method is implemented in a R-package, 'superdelta2', freely available at: https://github.com/fhlsjs/superdelta2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.