Unsupervised generative and graph representation learning for modelling cell differentiation.
Sci Rep
; 10(1): 9790, 2020 06 17.
Article
en En
| MEDLINE
| ID: mdl-32555334
ABSTRACT
Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells in a population, thus opening up the possibility of finding answers to biomedical questions about cell differentiation. In this paper, we explore unsupervised generative neural methods, based on the variational autoencoder, that can model cell differentiation by building meaningful representations from the high dimensional and complex gene expression data. We use disentanglement methods based on information theory to improve the data representation and achieve better separation of the biological factors of variation in the gene expression data. In addition, we use a graph autoencoder consisting of graph convolutional layers to predict relationships between single-cells. Based on these models, we develop a computational framework that consists of methods for identifying the cell types in the dataset, finding driver genes for the differentiation process and obtaining a better understanding of relationships between cells. We illustrate our methods on datasets from multiple species and also from different sequencing technologies.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Diferenciación Celular
/
Aprendizaje Automático
/
Modelos Biológicos
Tipo de estudio:
Evaluation_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2020
Tipo del documento:
Article
País de afiliación:
Reino Unido