Single-cell RNA-sequencing (scRNA-seq) can excavate cellular heterogeneity and distinguish different types of cells. Clustering cells into subpopulations is essential in analyzing scRNA-seq data as it can help subsequent downstream analysis. However, scRNA-seq data are high-dimensional, sparse, and contain erroneous zero counts, which poses a great challenge for clustering. Although various methods have emerged in recent years, they cannot fully grasp the information of cells by characterizing scRNA-seq data from a single perspective, resulting in poor learned embedding representation and poor clustering performance. In this paper, we propose a multi-view clustering framework scMVAF for scRNA-seq data, which can learn more discriminative embedding representations by integrating feature information from multiple cell views. First, to comprehensively capture the data information, we generate multiple diverse views by down-sampling features, and then scMVAF learns a strong embedding representation for each cell view using an autoencoder based on a denoising zero-inflated negative binomial model. Next, to explore the correlation between cells in different views, a multi-view fusion module is introduced to fuse the embeddings from different views into a unified feature space. Concurrently, the fused embeddings are clustered to generate pseudo labels to improve the embedding process, and finally updating the embedding features and pseudo-labels in turn to obtain better clustering performance. Experiments are implemented on 16 real datasets and verify that scMVAF is superior to the other eight advanced technologies. Our code script can be obtained at https://github.com/LQXLE/scMVAF/.
Wang et al. (Sat,) studied this question.