Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data

Abstract 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...

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Vydané v:Briefings in bioinformatics Ročník 22; číslo 4
Hlavní autori: Zuo, Chunman, Chen, Luonan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Oxford University Press 01.07.2021
Oxford Publishing Limited (England)
Predmet:
Accessibility
Chromatin
Datasets
Heterogeneity
Learning
Multilayers
Normal distribution
Probabilistic models
Problem Solving Protocol
Regulatory mechanisms (biology)
Transcription
Transcriptomes
single-cell multiple omics data
data integration
multimodal variational autoencoder
deep joint-learning model
ISSN:1467-5463, 1477-4054, 1477-4054
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Popis
Shrnutí:Abstract 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbaa287