ArticleNucleic acids research2019
Nonparametric expression analysis using inferential replicate counts.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed.
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- Long-read assembly reveals vast transcriptional complexity in the placenta associated with metabolic and endocrine function.Nature communications · 2026Article
- A multi-omics analysis of gut bacteriome, virome, and serum metabolome in bipolar depression.Npj mental health research · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- A systematic benchmark of high-accuracy PacBio long-read RNA sequencing for transcript-level quantification.Genome biology · 2026Article
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- YAP/TAZ-VGLL3 governs adipocyte fate via epigenetic reprogramming of PPARγ and its target enhancers.Science advances · 2026Article
- Isoforms in action: The impact of transcript diversity in phenotype.Genetics and molecular biology · 2026Article
- From bulk RNA sequencing to spatial transcriptomics: a comparative review of differential gene expression analysis methods.Human genomics · 2025Review
- Isoformic: a workflow for transcript-level RNA-seq interpretation.NAR genomics and bioinformatics · 2025Article
- Tree-based differential testing using inferential uncertainty for RNA-seq.Genome research · 2025Article
- LncRNA Foxo6os as a Novel " Scaffold" Mediates MYBPC3 in Combating Pathological Cardiac Hypertrophy and Heart Failure.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Sex-biased transcriptome in in vitro produced bovine early embryos.Cell & bioscience · 2025Article
- Oarfish: enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification.Bioinformatics (Oxford, England) · 2025Article
11 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases. Ideally, a statistical testing procedure should incorporate the inherent uncertainty of the abundance estimates arising from the quantification step. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts for each gene or transcript, and a subset of methods can incorporate uncertainty. Previous work has shown that nonparametric models for RNA-seq differential expression may have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account inferential uncertainty, leading to an inflated false discovery rate, in particular at the transcript level. We propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty. We compare our method, Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a single-cell RNA-seq dataset, assessing differential expression between sub-populations of cells, and compare its performance to the Wilcoxon test.
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