ScCCL: Single-Cell Data Clustering Based on Self-Supervised Contrastive Learning
Author:
Affiliation:
1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China
2. Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
Funder
National Key R&D Program of China
Shenzhen Science and Technology University Stable Support Program
Shenzhen Science and Technology Program
Basic and Applied Basic Research Foundation of Guangdong Province
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Genetics,Biotechnology
Link
http://xplorestaging.ieee.org/ielx7/8857/10144451/10032619.pdf?arnumber=10032619
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3. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
4. Algorithm AS 136: A K-means clustering algorithm;hartigan;J Roy Statist Soc Ser C (Appl Statist ),1979
5. Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning
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1. scBERC: A Batch Effect-Removed Clustering method for single-cell omics;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05
2. Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2023-11
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