Statistical methods for analysis of single-cell RNA-sequencing data

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput genomic technology used to study the expression dynamics of genes at single-cell level. Analyzing the scRNA-seq data in presence of biological confounding factors including dropout events is a challenging task. Thus, this article pre...

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Autores principales:	Samarendra Das, Ph.D., Shesh N. Rai, Ph.D.
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Zero inflated negative binomial model Molecular capture model Observed UMI count True UMI count Mean Zero Inflation Science Q
Acceso en línea:	https://doaj.org/article/ee7f54bcc86d434994791725df11b911
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spelling	oai:doaj.org-article:ee7f54bcc86d434994791725df11b9112021-11-24T04:31:19ZStatistical methods for analysis of single-cell RNA-sequencing data2215-016110.1016/j.mex.2021.101580https://doaj.org/article/ee7f54bcc86d434994791725df11b9112021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2215016121003708https://doaj.org/toc/2215-0161Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput genomic technology used to study the expression dynamics of genes at single-cell level. Analyzing the scRNA-seq data in presence of biological confounding factors including dropout events is a challenging task. Thus, this article presents a novel statistical approach for various analyses of the scRNA-seq Unique Molecular Identifier (UMI) counts data. The various analyses include modeling and fitting of observed UMI data, cell type detection, estimation of cell capture rates, estimation of gene specific model parameters, estimation of the sample mean and sample variance of the genes, etc. Besides, the developed approach is able to perform differential expression, and other downstream analyses that consider the molecular capture process in scRNA-seq data modeling. Here, the external spike-ins data can also be used in the approach for better results. The unique feature of the method is that it considers the biological process that leads to severe dropout events in modeling the observed UMI counts of genes.• The differential expression analysis of observed scRNA-seq UMI counts data is performed after adjustment for cell capture rates.• The statistical approach performs downstream differential zero inflation analysis, classification of influential genes, and selection of top marker genes.• Cell auxiliaries including cell clusters and other cell variables (e.g., cell cycle, cell phase) are used to remove unwanted variation to perform statistical tests reliably.Samarendra Das, Ph.D.Shesh N. Rai, Ph.D.ElsevierarticleZero inflated negative binomial modelMolecular capture modelObserved UMI countTrue UMI countMeanZero InflationScienceQENMethodsX, Vol 8, Iss , Pp 101580- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Zero inflated negative binomial model Molecular capture model Observed UMI count True UMI count Mean Zero Inflation Science Q
spellingShingle	Zero inflated negative binomial model Molecular capture model Observed UMI count True UMI count Mean Zero Inflation Science Q Samarendra Das, Ph.D. Shesh N. Rai, Ph.D. Statistical methods for analysis of single-cell RNA-sequencing data
description	Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput genomic technology used to study the expression dynamics of genes at single-cell level. Analyzing the scRNA-seq data in presence of biological confounding factors including dropout events is a challenging task. Thus, this article presents a novel statistical approach for various analyses of the scRNA-seq Unique Molecular Identifier (UMI) counts data. The various analyses include modeling and fitting of observed UMI data, cell type detection, estimation of cell capture rates, estimation of gene specific model parameters, estimation of the sample mean and sample variance of the genes, etc. Besides, the developed approach is able to perform differential expression, and other downstream analyses that consider the molecular capture process in scRNA-seq data modeling. Here, the external spike-ins data can also be used in the approach for better results. The unique feature of the method is that it considers the biological process that leads to severe dropout events in modeling the observed UMI counts of genes.• The differential expression analysis of observed scRNA-seq UMI counts data is performed after adjustment for cell capture rates.• The statistical approach performs downstream differential zero inflation analysis, classification of influential genes, and selection of top marker genes.• Cell auxiliaries including cell clusters and other cell variables (e.g., cell cycle, cell phase) are used to remove unwanted variation to perform statistical tests reliably.
format	article
author	Samarendra Das, Ph.D. Shesh N. Rai, Ph.D.
author_facet	Samarendra Das, Ph.D. Shesh N. Rai, Ph.D.
author_sort	Samarendra Das, Ph.D.
title	Statistical methods for analysis of single-cell RNA-sequencing data
title_short	Statistical methods for analysis of single-cell RNA-sequencing data
title_full	Statistical methods for analysis of single-cell RNA-sequencing data
title_fullStr	Statistical methods for analysis of single-cell RNA-sequencing data
title_full_unstemmed	Statistical methods for analysis of single-cell RNA-sequencing data
title_sort	statistical methods for analysis of single-cell rna-sequencing data
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/ee7f54bcc86d434994791725df11b911
work_keys_str_mv	AT samarendradasphd statisticalmethodsforanalysisofsinglecellrnasequencingdata AT sheshnraiphd statisticalmethodsforanalysisofsinglecellrnasequencingdata
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Statistical methods for analysis of single-cell RNA-sequencing data

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