Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of va...
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Gespeichert in:

Autor*in:	Jake Gagnon [verfasserIn] Lira Pi [verfasserIn] Matthew Ryals [verfasserIn] Qingwen Wan [verfasserIn] Wenxing Hu [verfasserIn] Zhengyu Ouyang [verfasserIn] Baohong Zhang [verfasserIn] Kejie Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	scRNA-seq single-cell RNA-seq DEG differential expression DE

Übergeordnetes Werk:	In: Life - MDPI AG, 2012, 12(2022), 6, p 850
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:6, p 850

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/life12060850

Katalog-ID:	DOAJ078965683

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allfieldsSound	10.3390/life12060850 doi (DE-627)DOAJ078965683 (DE-599)DOAJc154c563ce07478f9cfe8b1033529622 DE-627 ger DE-627 rakwb eng Jake Gagnon verfasserin aut Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals. scRNA-seq single-cell RNA-seq DEG differential expression DE Science Q Lira Pi verfasserin aut Matthew Ryals verfasserin aut Qingwen Wan verfasserin aut Wenxing Hu verfasserin aut Zhengyu Ouyang verfasserin aut Baohong Zhang verfasserin aut Kejie Li verfasserin aut In Life MDPI AG, 2012 12(2022), 6, p 850 (DE-627)718627156 (DE-600)2662250-6 20751729 nnns volume:12 year:2022 number:6, p 850 https://doi.org/10.3390/life12060850 kostenfrei https://doaj.org/article/c154c563ce07478f9cfe8b1033529622 kostenfrei https://www.mdpi.com/2075-1729/12/6/850 kostenfrei https://doaj.org/toc/2075-1729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 6, p 850
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author	Jake Gagnon
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topic_title	Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking scRNA-seq single-cell RNA-seq DEG differential expression DE
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title	Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking
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title_full	Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking
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title_sort	recommendations of scrna-seq differential gene expression analysis based on comprehensive benchmarking
title_auth	Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking
abstract	To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.
abstractGer	To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.
abstract_unstemmed	To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.
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title_short	Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking
url	https://doi.org/10.3390/life12060850 https://doaj.org/article/c154c563ce07478f9cfe8b1033529622 https://www.mdpi.com/2075-1729/12/6/850 https://doaj.org/toc/2075-1729
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author2	Lira Pi Matthew Ryals Qingwen Wan Wenxing Hu Zhengyu Ouyang Baohong Zhang Kejie Li
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up_date	2024-07-03T20:54:27.053Z
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