ArticleNature communications2024
Robust identification of perturbed cell types in single-cell RNA-seq data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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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
23 citing papers in PubMed.
- EBV reactivation priming of the peripheral immune system in multiple sclerosis relapse.Nature medicine · 2026Article
- Article
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- Single-cell profiling identifies obesity-disrupted brain glucose sensing.Nature communications · 2026Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- APOC1⁺IFI30⁺ TAMs orchestrate colorectal cancer liver metastasis by coupling cholesterol biosynthesis with secretion of immunosuppressive factors.Cellular and molecular life sciences : CMLS · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Characterizing gene perturbations in single cells via network divergence analysis.Nature communications · 2026Article
- Quantifying pathological progression from single-cell transcriptomic data with scPSS.Genome research · 2026Article
- Exploration of the mechanism of anlotinib in reversing PD-1 immunotherapy resistance: insights from single-cell sequencing.Cancer gene therapy · 2026Article
- Single-Cell Dissection of the SPP1-CD44 Axis Reveals Microglia-Astrocyte Crosstalk Driving Neuroinflammation in Temporal Lobe Epilepsy.Molecular neurobiology · 2026Article
- Article
- Single-Nucleus Transcriptomics Uncovers Xaf1-Driven PANoptosis as a Therapeutic Target in Aminoglycoside-Induced Hearing Loss.Cell proliferation · 2026Article
- Article
- HDAC2-mediated chromatin remodeling drives hepatocellular carcinoma progression: an integrative analysis of computational pathology and multi-transcriptomics.Journal of translational medicine · 2025Article
- Polystyrene nanoplastics disrupt epididymal initial segment by perturbing NK cell differentiation and epithelial homeostasis.Journal of nanobiotechnology · 2025Article
- Single-Cell transcriptomic profiles of peripheral blood immune cells reveal early monocyte and platelet activation in the transition from high-risk states to clinical sepsis.Scientific reports · 2025Article
- The Key Role of COA6 in Pancreatic Ductal Adenocarcinoma: Metabolic Reprogramming and Regulation of the Immune Microenvironment.Journal of cellular and molecular medicine · 2025Article
- Identification of immune-associated genes and single-cell sequencing analysis in diagnosing Behçet's disease.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.
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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.