关键词: 单细胞RNA测序, 无监督聚类, 并行计算, 细胞分型

Abstract: With the development of single-cell RNA sequencing（scRNA-seq） technology, the mainstream scRNA-seq throughput has grown from thousands of cells to tens of thousands of cells. Cell typing based on scRNA-seq data is one of the important problems in cell research, which mainly uses unsupervised clustering methods. The existing clustering methods for large-scale single-cell sequencing data reduce the time complexity by simplifying the single-cell network, which leads to the accuracy decline of cell typing. However, the common cell typing methods with high accuracy cannot handle large-scale data. For this reason, this study adopts the combination of [k]-nearest neighbors（KNN） and cell-cell similarity threshold to construct a new single-cell network, uses CPU+GPU heterogeneous parallel computing to improve the computing speed, and finally performs cell clustering by an improved Markov clustering algorithm. Through experiments on seven large-scale single-cell datasets, it is found that the algorithm has better clustering accuracy than the main algorithms, and thus is suitable for cell typing of scRNA-seq data produced by mainstream technologies.

Key words: single-cell RNA-seq, unsupervised clustering, parallel computing, cell typing