A semi-supervised non-negative matrix factorization model for scRNA-seq data analysis

Abstract

Single-cell RNA sequencing (scRNA-seq) technology enables the measurement of cellular gene expression at the single-cell level, thus facilitating cell clustering at the gene level. Despite numerous dimensionality reduction methods developed for scRNA-seq data, many are limited to analyzing individual gene expression matrices and struggle to address false positives and false zero expression entries effectively. Moreover, existing methods often underutilize prior knowledge of similarity and dissimilarity between multi-omics data, leading to the loss of intercellular correlations and shared structural information, thus hindering desired dimensionality reduction outcomes. To address these limitations, a novel model termed joint non-negative matrix factorization with similarity and dissimilarity constraints (SDJNMF) was proposed to tailor for scRNA-seq data clustering. The model leverages prior knowledge of similarity and dissimilarity across multiple gene expression matrices, facilitating joint non-negative matrix factorization to extract common features from multi-omics data. By preserving shared structural and cellular relevance information, SDJNMF enhances the clustering of similar cells while effectively separating dissimilar ones. Furthermore, the SDJNMF model incorporates sparse Singular Value Decomposition during initialization to mitigate noise and redundancy and ensure robust dimensionality reduction. The experimental results demonstrate that the SDJNMF model exhibits superior performance on the 10 datasets, not only outperforming the other 14 algorithms in terms of clustering accuracy on the 9 datasets, but also enhancing the $ARI$ of SDJNMF by an average of 0.0687 in comparison to the second-best algorithm on each dataset. In the visual representation, the model is able to efficiently and accurately cluster similar cells and effectively discriminate different classes of cells from each other. Additionally, the SDJNMF model was applied to identify informative genes and conduct enrichment analysis, validating that genes identified by SDJNMF significantly influence biological processes. Overall, the SDJNMF offers innovative tools for cell cluster identification and advances biological research. The source code of SDJNMF is available online at https://github.com/Jindsmu/SDJNMF.