Structure-preserving visualisation of high dimensional single-cell datasets

Single-cell technologies offer an unprecedented opportunity to effectively characterize cellular heterogeneity in health and disease. Nevertheless, visualisation and interpretation of these multi-dimensional datasets remains a challenge. We present a novel framework, ivis, for dimensionality reducti...

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Detalles Bibliográficos
Autores principales: Szubert, Benjamin, Cole, Jennifer E., Monaco, Claudia, Drozdov, Ignat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586841/
https://www.ncbi.nlm.nih.gov/pubmed/31222035
http://dx.doi.org/10.1038/s41598-019-45301-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586841/
https://www.ncbi.nlm.nih.gov/pubmed/31222035
http://dx.doi.org/10.1038/s41598-019-45301-0

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