ArticleNature communications2026
Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- ARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration.Bioinformatics (Oxford, England) · 2026Article
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Single-cell multi-omics technologies profile multiple molecular layers in individual cells, but existing methods often struggle to integrate transcriptomic, proteomic, and epigenomic measurements into an interpretable representation while preserving relationships among cells. Here, we present the single-cell multi-omics adversarial graph convolutional autoencoder (scMAGCA), which constructs cell graphs and uses adversarial alignment to learn interpretable shared embeddings that capture cellular heterogeneity and regulatory complexity. Across diverse datasets, scMAGCA outperforms existing methods in modality alignment, clustering, and batch correction. In Alzheimer's disease, scMAGCA resolves neuronal subtypes and regulatory programs that are missed by single-modality analyses. In kidney cancer, it identifies tumor-specific epithelial and endothelial populations and uncovers biomarkers validated by quantitative polymerase chain reaction. These results support scMAGCA as an interpretable framework for resolving complex cell states in disease.
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What Socratic 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 Socratic graph.