ArticleLife (Basel, Switzerland)2022
Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking.
Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Single-cell analysis of skin and blood reveals systemic immune responses to ultraviolet B and their impairment in MS.Journal of neuroinflammation · 2026Trial
- 4-1BBCell reports. Medicine · 2025Trial
- Assessing the impact of parental linear gene normalization on the performance of statistical models for circular RNA differential expression analysis.Briefings in bioinformatics · 2026Article
- Distinct single-nucleus RNA-seq changes among non-neuronal cells in ADNC, LATE-NC, and mixed pathologies.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- SpaMTP: integrative statistical analysis and visualization of spatial metabolomics and transcriptomics data.Nature methods · 2026Article
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- FLASH-MM: fast and scalable single-cell differential expression analysis using linear mixed-effects models.Nature communications · 2026Article
- Single-Cell Transcriptomics Reveals Macrophage Dysfunction and cDC2 Activation in COPD: Candidate Biomarkers for Individualized Nursing Care.International journal of chronic obstructive pulmonary disease · 2026Article
- Integrative Genomics Approach Identifies Glial Transcriptomic Dysregulation and Risk in the Cortex of Individuals With Alcohol Use Disorder.Biological psychiatry · 2026Article
- Differential expression analysis in single cell and spatial RNASeq without model assumptions.bioRxiv : the preprint server for biology · 2025Article
- Neural networks reveal novel gene signatures in Parkinson disease from single-nuclei transcriptomes.NPJ Parkinson's disease · 2025Article
- Comparative mapping of single-cell transcriptomic landscapes in neurodegenerative diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Generating human primordial germ cell-like cells from pluripotent stem cells: a scoping review ofHuman reproduction open · 2025Review
- Long-term, cell type-specific effects of prenatal stress on dorsal striatum and relevant behaviors in mice.bioRxiv : the preprint server for biology · 2024Article
- Comparative mapping of single-cell transcriptomic landscapes in neurodegenerative diseases.bioRxiv : the preprint server for biology · 2024Article
- Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.Genome research · 2024Article
- ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems.BMC bioinformatics · 2024Article
- Article
- Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE.Genome biology · 2024Article
- Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks.Acta neuropathologica communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.
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.