Cargando…
AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction
Single-cell RNA sequencing (scRNA-seq) technology has become an effective tool for high-throughout transcriptomic study, which circumvents the averaging artifacts corresponding to bulk RNA-seq technology, yielding new perspectives on the cellular diversity of potential superficially homogeneous popu...
Autores principales: | Zhao, Shuchang, Zhang, Li, Liu, Xuejun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Higher Education Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607720/ https://www.ncbi.nlm.nih.gov/pubmed/36320820 http://dx.doi.org/10.1007/s11704-022-2011-y |
Ejemplares similares
-
AutoImpute: Autoencoder based imputation of single-cell RNA-seq data
por: Talwar, Divyanshu, et al.
Publicado: (2018) -
AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders
por: Xu, Li, et al.
Publicado: (2021) -
Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-seq Data
por: Chi, Weilai, et al.
Publicado: (2020) -
Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks
por: Rao, Jiahua, et al.
Publicado: (2021) -
A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
por: Luo, Zixiang, et al.
Publicado: (2021)