Cargando…
Single-cell RNA-seq denoising using a deep count autoencoder
Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed. We propose a deep count...
Autores principales: | Eraslan, Gökcen, Simon, Lukas M., Mircea, Maria, Mueller, Nikola S., Theis, Fabian J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6344535/ https://www.ncbi.nlm.nih.gov/pubmed/30674886 http://dx.doi.org/10.1038/s41467-018-07931-2 |
Ejemplares similares
-
Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-seq Data
por: Chi, Weilai, et al.
Publicado: (2020) -
Sparse Convolutional Denoising Autoencoders for Genotype Imputation
por: Chen, Junjie, et al.
Publicado: (2019) -
Association Analysis of Deep Genomic Features Extracted by Denoising Autoencoders in Breast Cancer
por: Liu, Qian, et al.
Publicado: (2019) -
Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
por: Wang, Yuge, et al.
Publicado: (2022) -
Denoising of Optics Measurements Using Autoencoder Neural Networks
por: Fol, Elena, et al.
Publicado: (2021)