EmptyNN: A neural network based on positive and unlabeled learning to remove cell-free droplets and recover lost cells in scRNA-seq data

Droplet-based single-cell RNA sequencing (scRNA-seq) has significantly increased the number of cells profiled per experiment and revolutionized the study of individual transcriptomes. However, to maximize the biological signal, robust computational methods are needed to distinguish cell-free from ce...

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Detalles Bibliográficos
Autores principales: Yan, Fangfang, Zhao, Zhongming, Simon, Lukas M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8369248/
https://www.ncbi.nlm.nih.gov/pubmed/34430929
http://dx.doi.org/10.1016/j.patter.2021.100311

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8369248/
https://www.ncbi.nlm.nih.gov/pubmed/34430929
http://dx.doi.org/10.1016/j.patter.2021.100311

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