Abstract
Single-cell transcriptomics characterizes gene expression profiles at the single-cell level, offering an unprecedented opportunity to understand cellular systems. As a fundamental task in single-cell data analysis, cell clustering significantly contributes to identifying cellular heterogeneity, thereby affecting downstream analyses. A number of deep learning methods have been proposed for clustering single-cell RNA sequencing (scRNA-seq) data. However, the large parameter space makes these methods sensitive to parameter settings. To leverage the strong capabilities of deep learning in capturing complex structures in single-cell data while ensuring algorithmic robustness, we propose a contrastive ensemble learning method named scRECL for scRNA-seq data clustering. In our approach, Siamese neural networks are trained under various $k$-nearest neighbors partitions to obtain low-dimensional embeddings of the scRNA-seq data. Multiplex graphs in representative element selection help filter out noisy and redundant cells. Consequently, contrastive ensemble learning is performed for efficient and effective latent embedding, as well as robust analysis of cellular heterogeneity in scRNA-seq data.