CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity

MOTIVATION: Integration of growing single-cell RNA sequencing datasets helps better understand cellular identity and function. The major challenge for integration is removing batch effects while preserving biological heterogeneities. Advances in contrastive learning have inspired several contrastive...

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Detalles Bibliográficos
Autores principales: Yan, Xuhua, Zheng, Ruiqing, Wu, Fangxiang, Li, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9985174/
https://www.ncbi.nlm.nih.gov/pubmed/36821425
http://dx.doi.org/10.1093/bioinformatics/btad099

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9985174/
https://www.ncbi.nlm.nih.gov/pubmed/36821425
http://dx.doi.org/10.1093/bioinformatics/btad099

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