ArticlePloS one2020
Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed.
- Laryngeal Transcriptomic Insights into Echolocation Call Frequency Divergence in Closely RelatedBiology · 2026Article
- Revisiting differential expression analysis: An updated six-dimensional comparative study.PloS one · 2026Article
- Single Cell RNA Sequencing in Colorectal Cancer Immunology: Recent Updates, Application, and Emerging Challenges.Current molecular medicine · 2026Review
- Characterising commensal and pathogenic staphylococcal interactions with neonatal and adult blood.Scientific reports · 2025Article
- Robust and adaptive non-parametric tests for detecting general distributional shifts in gene expression.Cell reports methods · 2025Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- ChIPbinner: an R package for analyzing broad histone marks binned in uniform windows from ChIP-Seq or CUT&RUN/TAG data.BMC bioinformatics · 2025Article
- A practical guide to sequencing in neuropsychiatric research.NPP - digital psychiatry and neuroscience · 2025Review
- Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results.Nucleic acids research · 2024Article
- Article
- Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity.Cell biology and toxicology · 2024Article
- Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates.FEBS open bio · 2024Article
- BEERS2: RNA-Seq simulation through high fidelity in silico modeling.Briefings in bioinformatics · 2024Article
- Development of an optimized, non-stem cell line for intranasal delivery of therapeutic cargo to the central nervous system.Molecular oncology · 2024Article
- Critical Differential Expression Assessment for Individual Bulk RNA-Seq Projects.bioRxiv : the preprint server for biology · 2024Article
- Effects of antibiotic growth promoter and its natural alternative on poultry cecum ecosystem: an integrated analysis of gut microbiota and host expression.Frontiers in microbiology · 2024Article
- Searching for bacterial plastitrophs in modified Winogradsky columns.Frontiers in microbiomes · 2024Article
- Human and mouse neutrophils share core transcriptional programs in both homeostatic and inflamed contexts.Nature communications · 2023Article
- Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses.Nature medicine · 2023Article
- Gene augmentation for autosomal dominant retinitis pigmentosa using rhodopsin genomic loci nanoparticles in the P23HGene therapy · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
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.