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ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating indivi...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477531/ https://www.ncbi.nlm.nih.gov/pubmed/35924984 http://dx.doi.org/10.1093/bioinformatics/btac541 |