A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types

Recent advances in single cell RNA sequencing (scRNA-seq) technologies have been invaluable in the study of the diversity of cancer cells and the tumor microenvironment. While scRNA-seq platforms allow processing of a high number of cells, uneven read quality and technical artifacts hinder the abili...

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Detalles Bibliográficos
Autores principales: Bishara, Isaac, Chen, Jinfeng, Griffiths, Jason I., Bild, Andrea H., Nath, Aritro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732024/
https://www.ncbi.nlm.nih.gov/pubmed/36506328
http://dx.doi.org/10.3389/fgene.2022.982019

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732024/
https://www.ncbi.nlm.nih.gov/pubmed/36506328
http://dx.doi.org/10.3389/fgene.2022.982019

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