Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data

MOTIVATION: Dimensionality reduction is a key step in the analysis of single-cell RNA-sequencing data. It produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, hete...

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Detalles Bibliográficos
Autores principales: Angerer, Philipp, Fischer, David S, Theis, Fabian J, Scialdone, Antonio, Marr, Carsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520047/
https://www.ncbi.nlm.nih.gov/pubmed/32207520
http://dx.doi.org/10.1093/bioinformatics/btaa198

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520047/
https://www.ncbi.nlm.nih.gov/pubmed/32207520
http://dx.doi.org/10.1093/bioinformatics/btaa198

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