  Single-cell multi-view clustering enables the exploration of cellular
heterogeneity within the same cell from different views. Despite the
development of several multi-view clustering methods, two primary challenges
persist. Firstly, most existing methods treat the information from both
single-cell RNA (scRNA) and single-cell Assay of Transposase Accessible
Chromatin (scATAC) views as equally significant, overlooking the substantial
disparity in data richness between the two views. This oversight frequently
leads to a degradation in overall performance. Additionally, the majority of
clustering methods necessitate manual specification of the number of clusters
by users. However, for biologists dealing with cell data, precisely determining
the number of distinct cell types poses a formidable challenge. To this end, we
introduce scUNC, an innovative multi-view clustering approach tailored for
single-cell data, which seamlessly integrates information from different views
without the need for a predefined number of clusters. The scUNC method
comprises several steps: initially, it employs a cross-view fusion network to
create an effective embedding, which is then utilized to generate initial
clusters via community detection. Subsequently, the clusters are automatically
merged and optimized until no further clusters can be merged. We conducted a
comprehensive evaluation of scUNC using three distinct single-cell datasets.
The results underscored that scUNC outperforms the other baseline methods.
