有界函数
稳健性(进化)
聚类分析
计算机科学
柯西分布
约束(计算机辅助设计)
代表(政治)
数据挖掘
源代码
秩(图论)
算法
数学
人工智能
统计
数学分析
生物化学
化学
几何学
组合数学
政治
政治学
法学
基因
操作系统
作者
Qian Ding,Wenyi Yang,Meng Luo,Chang Xu,Zhaochun Xu,Fenglan Pang,Yideng Cai,A. S. Anashkina,Xi Su,Na Chen,Qinghua Jiang
摘要
Abstract The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.
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