Semisupervised Generative Autoencoder for Single-Cell Data.
J Comput Biol
; 27(8): 1190-1203, 2020 08.
Article
en En
| MEDLINE
| ID: mdl-31794242
ABSTRACT
Single-cell transcriptomics offers a tool to study the diversity of cell phenotypes through snapshots of the abundance of mRNA in individual cells. Often there is additional information available besides the single-cell gene expression counts, such as bulk transcriptome data from the same tissue, or quantification of surface protein levels from the same cells. In this study, we propose models based on the Bayesian deep learning approach, where protein quantification, available as CITE-seq counts, from the same cells is used to constrain the learning process, thus forming a SemI-SUpervised generative Autoencoder (SISUA) model. The generative model is based on the deep variational autoencoder (VAE) neural network architecture.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Biología Computacional
/
Análisis de la Célula Individual
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Transcriptoma
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
J Comput Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article