Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets

While the methods available for single-cell ATAC-seq analysis are well optimized for clustering cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration across scATAC-seq data...

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Detalles Bibliográficos
Autores principales: Erbe, Rossin, Kessler, Michael D, Favorov, Alexander V, Easwaran, Hariharan, Gaykalova, Daria A, Fertig, Elana J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Methods Online
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337516/
https://www.ncbi.nlm.nih.gov/pubmed/32392348
http://dx.doi.org/10.1093/nar/gkaa349

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337516/
https://www.ncbi.nlm.nih.gov/pubmed/32392348
http://dx.doi.org/10.1093/nar/gkaa349

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