BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell imputation

• Published in: Briefings in bioinformatics 2024-07, Vol.25 (5)
• Main Authors: Zhao, Jiaying, Ching, Wai-Ki, Wong, Chi-Wing, Cheng, Xiaoqing
• Format: Article
• Language: English
• Subjects: Algorithms; Availability; Clustering; Computational Biology - methods; Constraints; Datasets; Factorization; Gene Expression Profiling - methods; Gene Regulatory Networks; Gene sequencing; Graph theory; Heterogeneity; Humans; Information processing; Sequence Analysis, RNA - methods; Similarity; Single-Cell Analysis - methods; Software; Transcriptomes
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae432

Abstract Motivation Single cell RNA sequencing (scRNA-seq) technique enables the transcriptome profiling of hundreds to ten thousands of cells at the unprecedented individual level and provides new insights to study cell heterogeneity. However, its advantages are hampered by dropout events. To address this problem, we propose a Blockwise Accelerated Non-negative Matrix Factorization framework with Structural network constraints (BANMF-S) to impute those technical zeros. Results BANMF-S constructs a gene-gene similarity network to integrate prior information from the external PPI network by the Triadic Closure Principle and a cell-cell similarity network to capture the neighborhood structure and temporal information through a Minimum-Spanning Tree. By collaboratively employing these two networks as regularizations, BANMF-S encourages the coherence of similar gene and cell pairs in the latent space, enhancing the potential to recover the underlying features. Besides, BANMF-S adopts a blocklization strategy to solve the traditional NMF problem through distributed Stochastic Gradient Descent method in a parallel way to accelerate the optimization. Numerical experiments on simulations and real datasets verify that BANMF-S can improve the accuracy of downstream clustering and pseudo-trajectory inference, and its performance is superior to seven state-of-the-art algorithms. Availability All data used in this work are downloaded from publicly available data sources, and their corresponding accession numbers or source URLs are provided in Supplementary File Section 5.1 Dataset Information. The source codes are publicly available in Github repository https://github.com/jiayingzhao/BANMF-S.