Cargando…
scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot...
Autores principales: | Wang, Zile, Wang, Haiyun, Zhao, Jianping, Zheng, Chunhou |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214737/ https://www.ncbi.nlm.nih.gov/pubmed/37237310 http://dx.doi.org/10.1186/s12859-023-05339-4 |
Ejemplares similares
-
scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
por: Wang, HaiYun, et al.
Publicado: (2022) -
SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder
por: Zhao, Jianping, et al.
Publicado: (2021) -
scSemiAE: a deep model with semi-supervised learning for single-cell transcriptomics
por: Dong, Jiayi, et al.
Publicado: (2022) -
A robust semi-supervised NMF model for single cell RNA-seq data
por: Wu, Peng, et al.
Publicado: (2020) -
Contrastive self-supervised clustering of scRNA-seq data
por: Ciortan, Madalina, et al.
Publicado: (2021)