文档介绍：计算机工程与应用
Computer Engineering and Applications
ISSN 1002-8331,CN 11-2127/TP
法比现有主要算
法具有更好的聚类准确度，从而适合基于主流单细胞测序技术数据的细胞分型。
关键词：单细胞 RNA 测序; 无监督聚类; 并行计算; 细胞分型
文献标志码:A 中图分类号:TP391 doi：.1002--0340
Clustering of Single-Cell RNA-seq Data Based on Heterogeneous Parallel Computing
XIE Linjuan, LI Lixuan, ZHANG Shaoqiang
College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
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 theaccuracy 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 com-
bination of k-nearest neighbors (KNN) and cell-cell similarity threshold to construct a new single-cell netw