Cargando…
Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-S...
Autores principales: | van Unen, Vincent, Höllt, Thomas, Pezzotti, Nicola, Li, Na, Reinders, Marcel J. T., Eisemann, Elmar, Koning, Frits, Vilanova, Anna, Lelieveldt, Boudewijn P. F. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5700955/ https://www.ncbi.nlm.nih.gov/pubmed/29170529 http://dx.doi.org/10.1038/s41467-017-01689-9 |
Ejemplares similares
-
Mass cytometry reveals innate lymphoid cell differentiation pathways in the human fetal intestine
por: Li, Na, et al.
Publicado: (2018) -
CyTOFmerge: integrating mass cytometry data across multiple panels
por: Abdelaal, Tamim, et al.
Publicado: (2019) -
Predicting Cell Populations in Single Cell Mass Cytometry Data
por: Abdelaal, Tamim, et al.
Publicado: (2019) -
Interactive Visual
Exploration of 3D Mass Spectrometry
Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals
Spatiomolecular Structures at Full Data Resolution
por: Abdelmoula, Walid M., et al.
Publicado: (2018) -
BrainScope: interactive visual exploration of the spatial and temporal human brain transcriptome
por: Huisman, Sjoerd M.H., et al.
Publicado: (2017)