ArticleNucleic acids research2022
Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- scArchon: a scalable benchmarking framework for assessing single-cell perturbation models.Genome biology · 2026Article
- Human neuronal differentiation under Aβ exposure: a single-cell transcriptomic and epigenomic dataset.Scientific data · 2026Article
- Identification of Mood Disorders Causal Genes by Integrating the Brain Proteome and Transcriptome.Molecular neurobiology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- ScRNA-seq combined with ATAC-seq analysis to explore the metabolic balance mechanism of CCl4-induced liver inflammatory injury.Frontiers in immunology · 2025Article
- Unique challenges and best practices for single cell transcriptomic analysis in toxicology.Current opinion in toxicology · 2024Article
- scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.Bioinformatics (Oxford, England) · 2024Article
- Molecular analyses of zebrafish V0v spinal interneurons and identification of transcriptional regulators downstream of Evx1 and Evx2 in these cells.Neural development · 2023Article
- Generative modeling of single-cell gene expression for dose-dependent chemical perturbations.Patterns (New York, N.Y.) · 2023Article
- Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- Breaking bad: necroptosis in the pathogenesis of gastrointestinal diseases.Frontiers in immunology · 2023Review
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Authors and funding
7 authors.
Funding
Abstract
The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and toxicological responses. Current differential gene expression analysis (DGEA) methods focus primarily on two group comparisons, not multi-group dose-response study designs used in safety assessments. To benchmark DGEA methods for dose-response scRNAseq experiments, we proposed a multiplicity corrected Bayesian testing approach and compare it against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data. Our Bayesian test method outperformed all other tests for a broad range of accuracy metrics including control of false positive error rates. Most notable, the fit-for-purpose and standard multiple group DGEA methods were superior to the two group scRNAseq methods for dose-response study designs. Collectively, our benchmarking of DGEA methods demonstrates the importance in considering study design when determining the most appropriate test methods.
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Registered trials
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