矩阵分解
计算机科学
聚类分析
降维
插补(统计学)
人工智能
数据挖掘
非负矩阵分解
模式识别(心理学)
缺少数据
机器学习
特征向量
量子力学
物理
作者
Wei Lan,Jianwei Chen,Mingyang Liu,Qingfeng Chen,Jin Liu,Jianxin Wang,Yi‐Ping Phoebe Chen
出处
期刊:
日期:2024-04-12
卷期号:23 (2): 569-578
被引量:17
标识
DOI:10.1109/tcbb.2024.3387911
摘要
By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for clustering single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. The code can be downloaded from https://github.com/lanbiolab/DSINMF.
科研通智能强力驱动
Strongly Powered by AbleSci AI