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Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iterative...
| Autores principales: | , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214470/ https://www.ncbi.nlm.nih.gov/pubmed/32393754 http://dx.doi.org/10.1038/s41467-020-15851-3 |