VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics

Developing interpretable models is a major challenge in single cell deep learning. Here we show that the VEGA variational autoencoder model, whose decoder wiring mirrors gene modules, can provide direct interpretability to the latent space further enabling the inference of biological module activity...

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Autores principales:	Lucas Seninge, Ioannis Anastopoulos, Hongxu Ding, Joshua Stuart
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ed3aa6f7d98748388fc0f47ff767569f
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spelling	oai:doaj.org-article:ed3aa6f7d98748388fc0f47ff767569f2021-12-02T17:37:31ZVEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics10.1038/s41467-021-26017-02041-1723https://doaj.org/article/ed3aa6f7d98748388fc0f47ff767569f2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26017-0https://doaj.org/toc/2041-1723Developing interpretable models is a major challenge in single cell deep learning. Here we show that the VEGA variational autoencoder model, whose decoder wiring mirrors gene modules, can provide direct interpretability to the latent space further enabling the inference of biological module activity.Lucas SeningeIoannis AnastopoulosHongxu DingJoshua StuartNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Lucas Seninge Ioannis Anastopoulos Hongxu Ding Joshua Stuart VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
description	Developing interpretable models is a major challenge in single cell deep learning. Here we show that the VEGA variational autoencoder model, whose decoder wiring mirrors gene modules, can provide direct interpretability to the latent space further enabling the inference of biological module activity.
format	article
author	Lucas Seninge Ioannis Anastopoulos Hongxu Ding Joshua Stuart
author_facet	Lucas Seninge Ioannis Anastopoulos Hongxu Ding Joshua Stuart
author_sort	Lucas Seninge
title	VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
title_short	VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
title_full	VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
title_fullStr	VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
title_full_unstemmed	VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
title_sort	vega is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ed3aa6f7d98748388fc0f47ff767569f
work_keys_str_mv	AT lucasseninge vegaisaninterpretablegenerativemodelforinferringbiologicalnetworkactivityinsinglecelltranscriptomics AT ioannisanastopoulos vegaisaninterpretablegenerativemodelforinferringbiologicalnetworkactivityinsinglecelltranscriptomics AT hongxuding vegaisaninterpretablegenerativemodelforinferringbiologicalnetworkactivityinsinglecelltranscriptomics AT joshuastuart vegaisaninterpretablegenerativemodelforinferringbiologicalnetworkactivityinsinglecelltranscriptomics
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VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics

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