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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...
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| Yayımlandı: | Nat Commun |
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| Asıl Yazarlar: | , , |
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
Nature Publishing Group UK
2018
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| Konular: | |
| Online Erişim: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5962608/ https://ncbi.nlm.nih.gov/pubmed/29784946 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-018-04368-5 |
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