Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics

Huang et al apply and benchmark multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. Their approach reveals potential genome functions and gene regulatory mechanisms from gene expression to neu...

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Autores principales: Jiawei Huang, Jie Sheng, Daifeng Wang
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/216380d4edf44c03beae98a5f0ad5b06
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spelling oai:doaj.org-article:216380d4edf44c03beae98a5f0ad5b062021-11-21T12:08:27ZManifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics10.1038/s42003-021-02807-62399-3642https://doaj.org/article/216380d4edf44c03beae98a5f0ad5b062021-11-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02807-6https://doaj.org/toc/2399-3642Huang et al apply and benchmark multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. Their approach reveals potential genome functions and gene regulatory mechanisms from gene expression to neuronal electrophysiology.Jiawei HuangJie ShengDaifeng WangNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-10 (2021)
institution DOAJ
collection DOAJ
language EN
topic Biology (General)
QH301-705.5
spellingShingle Biology (General)
QH301-705.5
Jiawei Huang
Jie Sheng
Daifeng Wang
Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
description Huang et al apply and benchmark multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. Their approach reveals potential genome functions and gene regulatory mechanisms from gene expression to neuronal electrophysiology.
format article
author Jiawei Huang
Jie Sheng
Daifeng Wang
author_facet Jiawei Huang
Jie Sheng
Daifeng Wang
author_sort Jiawei Huang
title Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
title_short Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
title_full Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
title_fullStr Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
title_full_unstemmed Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
title_sort manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/216380d4edf44c03beae98a5f0ad5b06
work_keys_str_mv AT jiaweihuang manifoldlearninganalysissuggestsstrategiestoalignsinglecellmultimodaldataofneuronalelectrophysiologyandtranscriptomics
AT jiesheng manifoldlearninganalysissuggestsstrategiestoalignsinglecellmultimodaldataofneuronalelectrophysiologyandtranscriptomics
AT daifengwang manifoldlearninganalysissuggestsstrategiestoalignsinglecellmultimodaldataofneuronalelectrophysiologyandtranscriptomics
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