scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data

Author:

Han Xudong,Wang Bing,Situ Chenghao,Qi Yaling,Zhu Hui,Li Yan,Guo XuejiangORCID

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.

Funder

Key Technology Research and Development Program of Shandong

The Chinese National Natural Science Foundation

The fund from Health Commission of Jiangsu Province

Publisher

Public Library of Science (PLoS)

Subject

General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Neuroscience

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