Conference Paper Published

A Dual-Graph-Driven Non-Negative Matrix Factorization Model for Single-Cell Omics Analysis

Lan, J., Wang, N., & Deng, J.* (2026). A Dual-Graph-Driven Non-Negative Matrix Factorization Model for Single-Cell Omics Analysis. In Proceedings of 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1012-1017.

DOI: https://doi.org/10.1109/BIBM66473.2025.11357023

Abstract

The advancement of single-cell sequencing technology has provided unprecedented resolution for investigating cellular heterogeneity. Methods based on non-negative matrix factorization (NMF) and autoencoders are widely applied in single-cell sequencing analysis. However, current analytical models for single-cell sequencing data still face challenges such as high noise and limited applicability to specific scenarios, leading to suboptimal clustering performance. To address this issue, this study proposes an Autoencoder-like Dual-Graph Nonnegative Matrix Factorization (ADGNMF) model for single-cell multiomics analysis. The proposed method first modifies the joint NMF into an autoencoder-like architecture, followed by construction of multi-omics graph regularization and co-cluster graph regularization to enhance clustering performance and representational capability of the model. Experimental results on 8 multi-source transcriptomic datasets, 2 transcriptomic-epigenomic datasets, and 2 transcriptomic-proteomic datasets validate superior clustering performance and biological interpretability of the model. The source code of ADGNMF is available at https://github.com/jj-LanJADGNMF.

Keywords

Non-negative matrix factorization, Single-cell sequencing, Clustering, Multi-omics, Marker gene