A benchmark of batch-effect correction methods for single-cell RNA sequencing data

BACKGROUND: Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integrati...

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Detalles Bibliográficos
Autores principales: Tran, Hoa Thi Nhu, Ang, Kok Siong, Chevrier, Marion, Zhang, Xiaomeng, Lee, Nicole Yee Shin, Goh, Michelle, Chen, Jinmiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6964114/
https://www.ncbi.nlm.nih.gov/pubmed/31948481
http://dx.doi.org/10.1186/s13059-019-1850-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6964114/
https://www.ncbi.nlm.nih.gov/pubmed/31948481
http://dx.doi.org/10.1186/s13059-019-1850-9

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