ArticlePLoS biology2023
scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.
Article in PLoS biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 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
- Decoding apoptosis, ferroptosis, and inflammatory cell death in adenomyosis at single-cell resolution.Briefings in bioinformatics · 2026Article
- Multi-omics approaches reveal erythroid progenitor cell in cancer: from passive bystander to active player.Oncogene · 2026Review
- Integration of unpaired single cell omics data by deep transfer graph convolutional network.PLoS computational biology · 2025Article
- TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics.Briefings in bioinformatics · 2024Article
- scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data.GigaScience · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.
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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.