ArticleNature communications2023
Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve.
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 29 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
29 citing papers in PubMed.
- Deconformer: efficient cell-type composition profiling of cfRNA across diverse cell types.Briefings in bioinformatics · 2026Article
- Scoring gene importance by interpreting single-cell foundation models.Nature biotechnology · 2026Article
- DECODE: deep learning-based common deconvolution framework for various omics data.Nature methods · 2026Article
- Early Fc-effector antibody signatures impact COVID-19 disease trajectory.medRxiv : the preprint server for health sciences · 2026Article
- Dysregulated TGFβ-ERK Signaling Drives Aberrant Extracellular Matrix Production in Noonan Syndrome-Associated Pulmonary Valve Stenosis.bioRxiv : the preprint server for biology · 2026Article
- Single-cell transcriptomics of leukocyte-enriched human milk reveals a highly diverse and adapted myeloid compartment.PloS one · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Single-cell RNA sequencing highlights the role of epithelial-immune dual features of proximal tubule cells in BK polyomavirus nephropathy.Journal of virology · 2025Article
- Variable efficiency of nonsense-mediated mRNA decay across human tissues, tumors and individuals.Genome biology · 2025Article
- TNFR1-mediated senescence and lack of TNFR2-signaling limit human intervertebral disc cell repair potential in degenerative conditions.Osteoarthritis and cartilage · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Deep learning based deconvolution methods: A systematic review.Computational and structural biotechnology journal · 2025Review
- Enrichment of Cancer-Associated Fibroblasts, Macrophages, and Up-Regulated TNF-α Signaling in the Tumor Microenvironment of CMS4 Colorectal Peritoneal Metastasis.Cancer medicine · 2025Article
- Cycling alpha cells in regenerative drug-treated human pancreatic islets may serve as key beta cell progenitors.Cell reports. Medicine · 2024Article
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations.Clinical and experimental immunology · 2024Review
- Cell type-specific epigenetic priming of gene expression in nucleus accumbens by cocaine.Science advances · 2024Article
- Permethrin exposure primes neuroinflammatory stress response to drive depression-like behavior through microglial activation in a mouse model of Gulf War Illness.Journal of neuroinflammation · 2024Article
- High-Resolution Profiling of Human Vocal Fold Cellular Landscapes With Single-Nuclei RNA Sequencing.The Laryngoscope · 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
3 authors.
Funding
Abstract
We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context.
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.