Subtypes estimation in single-cell multi-omics data via S-multi-SNE

Single-cell RNA sequencing (scRNA-seq) has rapidly become an essential method in modern biology, as it allows global characterisation of transcriptomics in individual cells from different tissues, or organisms. scRNA-seq data are collected from a single individual organism and they are composed as mRNA count matrices with rows representing the cells and columns the genes. New technologies further allow multi-omics data to be obtained on the same set of single cells. In this paper, multi-SNE and S-multi-SNE, two multi-view dimensionality reduction approaches, are adapted for visualization and classification of cellular subtypes, two important challenges in the analysis of scRNA-seq data. S-multi-SNE has been adapted for cell subtype estimation of scRNA-seq data. In a series of experiments we illustrate that it is possible to identify cell subtypes by leveraging information from individuals from the same species, from different species, and when data are generated by different sequencing technologies. In the conducted analyses, we show that S-multi-SNE consistently outperformed several other machine learning techniques and, in most comparisons, surpassed existing reference-based single-cell classification algorithms. We further illustrate how two single-cell multi-omics datasets, scATAC-seq and scRNA-seq, can be integrated together through S-multi-SNE for improved cell subtype identification.