ArticleNature communications2023
JOINTLY: interpretable joint clustering of single-cell transcriptomes.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution.Nature metabolism · 2025Guideline
- Single nucleus multiomics reveals an early inflammatory response to high-fat diet in mouse islets.Life science alliance · 2026Article
- Article
- Age-dependent progenitor switching shapes adult brown adipose tissue heterogeneity.Research square · 2026Article
- ASTRO: Automated Spatial-Transcriptome whole RNA Output.Bioinformatics (Oxford, England) · 2026Article
- Single-cell-resolved transcriptional dynamics of human subcutaneous adipose tissue during lifestyle- and bariatric surgery-induced weight loss.Nature metabolism · 2026Article
- UniMap: Type-Level Integration Enhances Biological Preservation and Interpretability in Single-Cell Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Combining transcriptomic and metabolomic insights to guide the clinical application of adipose- and bone marrow-derived mesenchymal stem cells.Medical review (2021) · 2025Article
- Dissecting human adipose tissue heterogeneity using single-cell omics technologies.Stem cell research & therapy · 2024Review
- Loss ofbioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 3 countries.
Funding
Abstract
Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) is increasingly being used to characterise the transcriptomic state of cell types at homeostasis, during development and in disease. However, this is a challenging task, as biological effects can be masked by technical variation. Here, we present JOINTLY, an algorithm enabling joint clustering of sxRNA-seq datasets across batches. JOINTLY performs on par or better than state-of-the-art batch integration methods in clustering tasks and outperforms other intrinsically interpretable methods. We demonstrate that JOINTLY is robust against over-correction while retaining subtle cell state differences between biological conditions and highlight how the interpretation of JOINTLY can be used to annotate cell types and identify active signalling programs across cell types and pseudo-time. Finally, we use JOINTLY to construct a reference atlas of white adipose tissue (WATLAS), an expandable and comprehensive community resource, in which we describe four adipocyte subpopulations and map compositional changes in obesity and between depots.
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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.