Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional visualizations that contribute to the human understanding of data. Existing algori...

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Detalles Bibliográficos
Autores principales: Amouzgar, Meelad, Glass, David R., Baskar, Reema, Averbukh, Inna, Kimmey, Samuel C., Tsai, Albert G., Hartmann, Felix J., Bendall, Sean C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403402/
https://www.ncbi.nlm.nih.gov/pubmed/36033591
http://dx.doi.org/10.1016/j.patter.2022.100536

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403402/
https://www.ncbi.nlm.nih.gov/pubmed/36033591
http://dx.doi.org/10.1016/j.patter.2022.100536

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