Cargando…
NISC: Neural Network-Imputation for Single-Cell RNA Sequencing and Cell Type Clustering
Single-cell RNA sequencing (scRNA-seq) reveals the transcriptome diversity in heterogeneous cell populations as it allows researchers to study gene expression at single-cell resolution. The latest advances in scRNA-seq technology have made it possible to profile tens of thousands of individual cells...
Autores principales: | Zhang, Xiang, Chen, Zhuo, Bhadani, Rahul, Cao, Siyang, Lu, Meng, Lytal, Nicholas, Chen, Yin, An, Lingling |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110639/ https://www.ncbi.nlm.nih.gov/pubmed/35591853 http://dx.doi.org/10.3389/fgene.2022.847112 |
Ejemplares similares
-
Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey
por: Lytal, Nicholas, et al.
Publicado: (2020) -
Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
por: Bhadani, Rahul, et al.
Publicado: (2023) -
AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders
por: Xu, Li, et al.
Publicado: (2021) -
McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data
por: Mongia, Aanchal, et al.
Publicado: (2019) -
Strategies for Obtaining and Pruning Imputed Whole-Genome Sequence Data for Genomic Prediction
por: Ye, Shaopan, et al.
Publicado: (2019)