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Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962608/ https://www.ncbi.nlm.nih.gov/pubmed/29784946 http://dx.doi.org/10.1038/s41467-018-04368-5 |