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Nonnegative spatial factorization applied to spatial genomics

Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incor...

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Detalles Bibliográficos
Autores principales: Townes, F. William, Engelhardt, Barbara E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911348/
https://www.ncbi.nlm.nih.gov/pubmed/36587187
http://dx.doi.org/10.1038/s41592-022-01687-w