Multi-domain translation between single-cell imaging and sequencing data using autoencoders

Integration of single cell data modalities increases the richness of information about the heterogeneity of cell states, but integration of imaging and transcriptomics is an open challenge. Here the authors use autoencoders to learn a probabilistic coupling and map these modalities to a shared laten...

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Autores principales:	Karren Dai Yang, Anastasiya Belyaeva, Saradha Venkatachalapathy, Karthik Damodaran, Abigail Katcoff, Adityanarayanan Radhakrishnan, G. V. Shivashankar, Caroline Uhler
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/7df126930d194ef68d3d6f0c6ad92f1f
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spelling	oai:doaj.org-article:7df126930d194ef68d3d6f0c6ad92f1f2021-12-02T11:45:56ZMulti-domain translation between single-cell imaging and sequencing data using autoencoders10.1038/s41467-020-20249-22041-1723https://doaj.org/article/7df126930d194ef68d3d6f0c6ad92f1f2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20249-2https://doaj.org/toc/2041-1723Integration of single cell data modalities increases the richness of information about the heterogeneity of cell states, but integration of imaging and transcriptomics is an open challenge. Here the authors use autoencoders to learn a probabilistic coupling and map these modalities to a shared latent space.Karren Dai YangAnastasiya BelyaevaSaradha VenkatachalapathyKarthik DamodaranAbigail KatcoffAdityanarayanan RadhakrishnanG. V. ShivashankarCaroline UhlerNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Karren Dai Yang Anastasiya Belyaeva Saradha Venkatachalapathy Karthik Damodaran Abigail Katcoff Adityanarayanan Radhakrishnan G. V. Shivashankar Caroline Uhler Multi-domain translation between single-cell imaging and sequencing data using autoencoders
description	Integration of single cell data modalities increases the richness of information about the heterogeneity of cell states, but integration of imaging and transcriptomics is an open challenge. Here the authors use autoencoders to learn a probabilistic coupling and map these modalities to a shared latent space.
format	article
author	Karren Dai Yang Anastasiya Belyaeva Saradha Venkatachalapathy Karthik Damodaran Abigail Katcoff Adityanarayanan Radhakrishnan G. V. Shivashankar Caroline Uhler
author_facet	Karren Dai Yang Anastasiya Belyaeva Saradha Venkatachalapathy Karthik Damodaran Abigail Katcoff Adityanarayanan Radhakrishnan G. V. Shivashankar Caroline Uhler
author_sort	Karren Dai Yang
title	Multi-domain translation between single-cell imaging and sequencing data using autoencoders
title_short	Multi-domain translation between single-cell imaging and sequencing data using autoencoders
title_full	Multi-domain translation between single-cell imaging and sequencing data using autoencoders
title_fullStr	Multi-domain translation between single-cell imaging and sequencing data using autoencoders
title_full_unstemmed	Multi-domain translation between single-cell imaging and sequencing data using autoencoders
title_sort	multi-domain translation between single-cell imaging and sequencing data using autoencoders
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7df126930d194ef68d3d6f0c6ad92f1f
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Multi-domain translation between single-cell imaging and sequencing data using autoencoders

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