Unsupervised generative and graph representation learning for modelling cell differentiation
| Autor: | Helena Andrés-Terré, Ana Cvejic, Ioana Bica, Pietro Liò |
|---|---|
| Přispěvatelé: | Cvejic, Ana [0000-0003-3204-9311], Apollo - University of Cambridge Repository |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Statistical methods
Computer science Cellular differentiation Science Population Datasets as Topic Gene Expression Information theory Machine learning computer.software_genre Models Biological 38 Machine Learning 03 medical and health sciences 0302 clinical medicine 631/114/2397 Animals Humans Computational models RNA-Seq 129 education 030304 developmental biology 45/91 0303 health sciences education.field_of_study Models Statistical Multidisciplinary business.industry article Cell Differentiation 631/114/1305 631/114/2415 Autoencoder Graph (abstract data type) Medicine Artificial intelligence 45/100 119 business computer 030217 neurology & neurosurgery Generative grammar |
| Zdroj: | Scientific Reports, Vol 10, Iss 1, Pp 1-13 (2020) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |