New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex data...

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Detalles Bibliográficos
Autores principales: Greene, Evan, Finak, Greg, D'Amico, Leonard A., Bhardwaj, Nina, Church, Candice D., Morishima, Chihiro, Ramchurren, Nirasha, Taube, Janis M., Nghiem, Paul T., Cheever, Martin A., Fling, Steven P., Gottardo, Raphael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672150/
https://www.ncbi.nlm.nih.gov/pubmed/34950900
http://dx.doi.org/10.1016/j.patter.2021.100372

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672150/
https://www.ncbi.nlm.nih.gov/pubmed/34950900
http://dx.doi.org/10.1016/j.patter.2021.100372

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