Model-based prediction of spatial gene expression via generative linear mapping

Single cell RNA-seq loses spatial information of gene expression in multicellular systems because tissue must be dissociated. Here, the authors show the spatial gene expression profiles can be both accurately and robustly reconstructed by a new computational method using a generative linear mapping,...

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Main Authors:	Yasushi Okochi, Shunta Sakaguchi, Ken Nakae, Takefumi Kondo, Honda Naoki
Format:	article
Language:	EN
Published:	Nature Portfolio 2021
Subjects:	Science Q
Online Access:	https://doaj.org/article/abbd231b7f69497dae153503ce587d0f
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id	oai:doaj.org-article:abbd231b7f69497dae153503ce587d0f
record_format	dspace
spelling	oai:doaj.org-article:abbd231b7f69497dae153503ce587d0f2021-12-02T16:04:25ZModel-based prediction of spatial gene expression via generative linear mapping10.1038/s41467-021-24014-x2041-1723https://doaj.org/article/abbd231b7f69497dae153503ce587d0f2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-24014-xhttps://doaj.org/toc/2041-1723Single cell RNA-seq loses spatial information of gene expression in multicellular systems because tissue must be dissociated. Here, the authors show the spatial gene expression profiles can be both accurately and robustly reconstructed by a new computational method using a generative linear mapping, Perler.Yasushi OkochiShunta SakaguchiKen NakaeTakefumi KondoHonda NaokiNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yasushi Okochi Shunta Sakaguchi Ken Nakae Takefumi Kondo Honda Naoki Model-based prediction of spatial gene expression via generative linear mapping
description	Single cell RNA-seq loses spatial information of gene expression in multicellular systems because tissue must be dissociated. Here, the authors show the spatial gene expression profiles can be both accurately and robustly reconstructed by a new computational method using a generative linear mapping, Perler.
format	article
author	Yasushi Okochi Shunta Sakaguchi Ken Nakae Takefumi Kondo Honda Naoki
author_facet	Yasushi Okochi Shunta Sakaguchi Ken Nakae Takefumi Kondo Honda Naoki
author_sort	Yasushi Okochi
title	Model-based prediction of spatial gene expression via generative linear mapping
title_short	Model-based prediction of spatial gene expression via generative linear mapping
title_full	Model-based prediction of spatial gene expression via generative linear mapping
title_fullStr	Model-based prediction of spatial gene expression via generative linear mapping
title_full_unstemmed	Model-based prediction of spatial gene expression via generative linear mapping
title_sort	model-based prediction of spatial gene expression via generative linear mapping
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/abbd231b7f69497dae153503ce587d0f
work_keys_str_mv	AT yasushiokochi modelbasedpredictionofspatialgeneexpressionviagenerativelinearmapping AT shuntasakaguchi modelbasedpredictionofspatialgeneexpressionviagenerativelinearmapping AT kennakae modelbasedpredictionofspatialgeneexpressionviagenerativelinearmapping AT takefumikondo modelbasedpredictionofspatialgeneexpressionviagenerativelinearmapping AT hondanaoki modelbasedpredictionofspatialgeneexpressionviagenerativelinearmapping
_version_	1718385226402496512

Model-based prediction of spatial gene expression via generative linear mapping

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