ArticleNature communications2023
Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.
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 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data.Advanced biotechnology · 2026Article
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- PAIR: Reconstructing Single-Cell Open-Chromatin Landscapes for Transcription Factor Regulome Mapping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- IST: an ontology-guided attention-based autoencoder for interpretable analysis of single-cell transcriptomic data.Scientific reports · 2026Article
- scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data.BMC biology · 2026Article
- scAURA: Alignment- and Uniformity-based Graph Debiased Contrastive Representation Architecture for Self-Supervised Clustering of Single-Cell Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- scHSC: enhancing single-cell RNA-seq clustering via hard sample contrastive learning.Briefings in bioinformatics · 2025Article
- scTsI: an effective two-stage imputation method for single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Topological identification and interpretation for single-cell epigenetic regulation elucidation in multi-tasks using scAGDE.Nature communications · 2025Article
- Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- scGraphformer: unveiling cellular heterogeneity and interactions in scRNA-seq data using a scalable graph transformer network.Communications biology · 2024Article
- scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.Briefings in bioinformatics · 2024Article
- Associating transcription factors to single-cell trajectories with DREAMIT.Genome biology · 2024Article
- SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.Cell reports methods · 2024Article
- PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data.Bioinformatics (Oxford, England) · 2024Article
- DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering.Scientific reports · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.
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.