Cargando…
Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data
A fundamental task in single-cell RNA-seq (scRNA-seq) analysis is the identification of transcriptionally distinct groups of cells. Numerous methods have been proposed for this problem, with a recent focus on methods for the cluster analysis of ultralarge scRNA-seq data sets produced by droplet-base...
Autores principales: | Do, Van Hoan, Rojas Ringeling, Francisca, Canzar, Stefan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015854/ https://www.ncbi.nlm.nih.gov/pubmed/33627473 http://dx.doi.org/10.1101/gr.267906.120 |
Ejemplares similares
-
A generalization of t-SNE and UMAP to single-cell multimodal omics
por: Do, Van Hoan, et al.
Publicado: (2021) -
SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data
por: Ren, Xianwen, et al.
Publicado: (2019) -
SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection
por: Wan, Shibiao, et al.
Publicado: (2020) -
scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data
por: Wang, Zongqin, et al.
Publicado: (2023) -
Large-scale protein function prediction using heterogeneous ensembles
por: Wang, Linhua, et al.
Publicado: (2018)