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Please use this identifier to cite or link to this item: http://krishi.icar.gov.in/jspui/handle/123456789/68627
Title: Statistical methods for single-cell RNA-sequencing data analysis.
Other Titles: Not Available
Authors: Samarendra Das
Shesh N. Rai
ICAR Data Use Licennce: http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf
Author's Affiliated institute: ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
University of Louisville, USA
Published/ Complete Date: 2021-11-01
Project Code: Not Available
Keywords: Zero inflated negative binomial model
Molecular capture model
Observed UMI count
True UMI count
Mean
Zero Inflation
Overdispersion
Publisher: Elsevier
Citation: Das, S. and Rai, S.N. (2021). Statistical methods for single-cell RNA-sequencing data analysis. MethodsX, 8, 101580. doi.org/10.1016/j.mex.2021.101580
Series/Report no.: Not Available;
Abstract/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.
Description: Not Available
ISSN: Not Available
Type(s) of content: Research Paper
Sponsors: Samarendra Das: Indian Council of Agricultural Research (ICAR), New Delhi, India (Netaji Subhas-ICAR International Fellowship, OM No. 18(02)/2016-EQR/Edn), ICAR-Indian Agricultural Statistics Research Institute (ICAR-IASRI), New Delhi, India.
Language: English
Name of Journal: MethodsX
Journal Type: research paper
Volume No.: 8
Page Number: 101580
Name of the Division/Regional Station: Not Available
Source, DOI or any other URL: doi.org/10.1016/j.mex.2021.101580
URI: http://krishi.icar.gov.in/jspui/handle/123456789/68627
Appears in Collections:AEdu-IASRI-Publication

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