Cargando…
Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization
Single cell RNA-sequencing (scRNA-seq) technology, a powerful tool for analyzing the entire transcriptome at single cell level, is receiving increasing research attention. The presence of dropouts is an important characteristic of scRNA-seq data that may affect the performance of downstream analyses...
Autores principales: | Zhang, Shuqin, Yang, Liu, Yang, Jinwen, Lin, Zhixiang, Ng, Michael K |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671375/ https://www.ncbi.nlm.nih.gov/pubmed/33575614 http://dx.doi.org/10.1093/nargab/lqaa064 |
Ejemplares similares
-
Assessing aeromedical risk: a three-dimensional risk matrix approach
por: Gray, Gary, et al.
Publicado: (2019) -
Amalgams: data-driven amalgamation for the dimensionality reduction of compositional data
por: Quinn, Thomas P, et al.
Publicado: (2020) -
Differential analysis of binarized single-cell RNA sequencing data captures biological variation
por: Bouland, Gerard A, et al.
Publicado: (2021) -
Deep soft K-means clustering with self-training for single-cell RNA sequence data
por: Chen, Liang, et al.
Publicado: (2020) -
Influenza A virus evolution and spatio-temporal dynamics in Eurasian wild birds: a phylogenetic and phylogeographical study of whole-genome sequence data
por: Lewis, Nicola S., et al.
Publicado: (2015)