Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

We present a novel self-supervised Contrastive LEArning framework for single-cell ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream analysis. Compared with current methods, CLEAR overcomes the heterogeneity of the experimental data with a specifically designed represen...

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Detalles Bibliográficos
Autores principales: Han, Wenkai, Cheng, Yuqi, Chen, Jiayang, Zhong, Huawen, Hu, Zhihang, Chen, Siyuan, Zong, Licheng, Hong, Liang, Chan, Ting-Fung, King, Irwin, Gao, Xin, Li, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Problem Solving Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9487595/
https://www.ncbi.nlm.nih.gov/pubmed/36089561
http://dx.doi.org/10.1093/bib/bbac377

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9487595/
https://www.ncbi.nlm.nih.gov/pubmed/36089561
http://dx.doi.org/10.1093/bib/bbac377

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