计算机科学
矩阵分解
稳健性(进化)
非负矩阵分解
聚类分析
双聚类
维数之咒
地点
模式识别(心理学)
数据挖掘
光谱聚类
稳健主成分分析
源代码
秩(图论)
可扩展性
人工智能
主成分分析
数学
相关聚类
CURE数据聚类算法
特征向量
组合数学
基因
物理
量子力学
生物化学
操作系统
化学
语言学
数据库
哲学
作者
Wei Zhang,Xiaoli Xue,Xiaoying Zheng,Zizhu Fan
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-03-01
卷期号:26 (3): 1394-1405
被引量:4
标识
DOI:10.1109/jbhi.2021.3099127
摘要
Fast-developing single-cell technologies create unprecedented opportunities to reveal cell heterogeneity and diversity. Accurate classification of single cells is a critical prerequisite for recovering the mechanisms of heterogeneity. However, the scRNA-seq profiles we obtained at present have high dimensionality, sparsity, and noise, which pose challenges for existing clustering methods in grouping cells that belong to the same subpopulation based on transcriptomic profiles. Although many computational methods have been proposed developing novel and effective computational methods to accurately identify cell types remains a considerable challenge. We present a new computational framework to identify cell types by integrating low-rank representation (LRR) and nonnegative matrix factorization (NMF); this framework is named NMFLRR. The LRR captures the global properties of original data by using nuclear norms, and a locality constrained graph regularization term is introduced to characterize the data's local geometric information. The similarity matrix and low-dimensional features of data can be simultaneously obtained by applying the alternating direction method of multipliers (ADMM) algorithm to handle each variable alternatively in an iterative way. We finally obtained the predicted cell types by using a spectral algorithm based on the optimized similarity matrix. Nine real scRNA-seq datasets were used to test the performance of NMFLRR and fifteen other competitive methods, and the accuracy and robustness of the simulation results suggest the NMFLRR is a promising algorithm for the classification of single cells. The simulation code is freely available at: https://github.com/wzhangwhu/NMFLRR_code.
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