Semisupervised Generative Autoencoder for Single-Cell Data

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...

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Veröffentlicht in:Journal of computational biology Jg. 27; H. 8; S. 1190
Hauptverfasser: Trong, Trung Ngo, Mehtonen, Juha, González, Gerardo, Kramer, Roger, Hautamäki, Ville, Heinäniemi, Merja
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.08.2020
Schlagworte:
generative
deep learning
autoencoder
variational
protein
semisupervised
single-cell
ISSN:1557-8666, 1557-8666
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Zusammenfassung: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. 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.
Bibliographie:ObjectType-Article-1
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ISSN:1557-8666
1557-8666
DOI:10.1089/cmb.2019.0337