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ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
BACKGROUND: With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect do...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628418/ https://www.ncbi.nlm.nih.gov/pubmed/34844559 http://dx.doi.org/10.1186/s12864-021-08101-3 |