Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data
| Autor: | Luonan Chen, Chunman Zuo |
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| Rok vydání: | 2020 |
| Předmět: |
Databases
Factual AcademicSubjects/SCI01060 Computer science computer.software_genre Models Biological 03 medical and health sciences Deep Learning 0302 clinical medicine Single cell transcriptome Humans Profiling (information science) deep joint-learning model data integration Molecular Biology 030304 developmental biology 0303 health sciences multimodal variational autoencoder Probabilistic logic Mixture model Autoencoder Chromatin single-cell multiple omics data Cellular heterogeneity Problem Solving Protocol Data mining Single-Cell Analysis K562 Cells Transcriptome computer 030217 neurology & neurosurgery Information Systems Data integration |
| Zdroj: | Briefings in Bioinformatics |
| ISSN: | 1477-4054 1467-5463 |
| DOI: | 10.1093/bib/bbaa287 |
| Popis: | Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent sparse and heterogeneous data are lacking. Here, we present a single-cell multimodal variational autoencoder model, which combines three types of joint-learning strategies with a probabilistic Gaussian Mixture Model to learn the joint latent features that accurately represent these multilayer profiles. Studies on both simulated datasets and real datasets demonstrate that it has more preferable capability (i) dissecting cellular heterogeneity in the joint-learning space, (ii) denoising and imputing data and (iii) constructing the association between multilayer omics data, which can be used for understanding transcriptional regulatory mechanisms. |
| Databáze: | OpenAIRE |
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