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Demystifying “drop-outs” in single-cell UMI data
Many existing pipelines for scRNA-seq data apply pre-processing steps such as normalization or imputation to account for excessive zeros or “drop-outs." Here, we extensively analyze diverse UMI data sets to show that clustering should be the foremost step of the workflow. We observe that most d...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412673/ https://www.ncbi.nlm.nih.gov/pubmed/32762710 http://dx.doi.org/10.1186/s13059-020-02096-y |