NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

The application of negative binomial mixed models (NBMMs) to single-cell data is computationally demanding. To address this issue, Liang He et al. have developed NEBULA, an efficient algorithm that can analyze differential gene expression or co-expression networks in multi-subject single-cell data s...

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Autores principales:	Liang He, Jose Davila-Velderrain, Tomokazu S. Sumida, David A. Hafler, Manolis Kellis, Alexander M. Kulminski
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1604a175b02d438a8389ecd99650f39c
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spelling	oai:doaj.org-article:1604a175b02d438a8389ecd99650f39c2021-12-02T14:47:37ZNEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data10.1038/s42003-021-02146-62399-3642https://doaj.org/article/1604a175b02d438a8389ecd99650f39c2021-05-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02146-6https://doaj.org/toc/2399-3642The application of negative binomial mixed models (NBMMs) to single-cell data is computationally demanding. To address this issue, Liang He et al. have developed NEBULA, an efficient algorithm that can analyze differential gene expression or co-expression networks in multi-subject single-cell data sets, and validate it on snRNA-seq and scRNA-seq data sets comprising ~200k cells from cohorts of Alzheimer’s disease and multiple sclerosis patients.Liang HeJose Davila-VelderrainTomokazu S. SumidaDavid A. HaflerManolis KellisAlexander M. KulminskiNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Liang He Jose Davila-Velderrain Tomokazu S. Sumida David A. Hafler Manolis Kellis Alexander M. Kulminski NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
description	The application of negative binomial mixed models (NBMMs) to single-cell data is computationally demanding. To address this issue, Liang He et al. have developed NEBULA, an efficient algorithm that can analyze differential gene expression or co-expression networks in multi-subject single-cell data sets, and validate it on snRNA-seq and scRNA-seq data sets comprising ~200k cells from cohorts of Alzheimer’s disease and multiple sclerosis patients.
format	article
author	Liang He Jose Davila-Velderrain Tomokazu S. Sumida David A. Hafler Manolis Kellis Alexander M. Kulminski
author_facet	Liang He Jose Davila-Velderrain Tomokazu S. Sumida David A. Hafler Manolis Kellis Alexander M. Kulminski
author_sort	Liang He
title	NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
title_short	NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
title_full	NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
title_fullStr	NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
title_full_unstemmed	NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
title_sort	nebula is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1604a175b02d438a8389ecd99650f39c
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NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

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