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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

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