Cargando…
Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene...
Autores principales: | Wang, Yuge, Zhao, Hongyu |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007392/ https://www.ncbi.nlm.nih.gov/pubmed/35363784 http://dx.doi.org/10.1371/journal.pcbi.1010025 |
Ejemplares similares
-
VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder
por: Wang, Dongfang, et al.
Publicado: (2018) -
Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis
por: Geddes, Thomas A., et al.
Publicado: (2019) -
Single-cell RNA-seq denoising using a deep count autoencoder
por: Eraslan, Gökcen, et al.
Publicado: (2019) -
AutoImpute: Autoencoder based imputation of single-cell RNA-seq data
por: Talwar, Divyanshu, et al.
Publicado: (2018) -
Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
por: Feng, Xiang, et al.
Publicado: (2022)