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  2. Agricultural Education A1
  3. ICAR-Indian Agricultural Statistics Research Institute B7
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Please use this identifier to cite or link to this item: http://krishi.icar.gov.in/jspui/handle/123456789/68628
Title: SwarnSeq: An Improved Statistical Approach for Differential Expression Analysis of Single-Cell RNA-Seq Data.
Other Titles: Not Available
Authors: Samarendra Das
S.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: 2021-05-01
Project Code: Not Available
Keywords: SwarnSeq
scRNA-seq
Zero inflated negative binomial
Dispersion
Differential expression
Capture rates
Publisher: Elsevier
Citation: Das, S. and Rai, S.N. (2021). SwarnSeq: An Improved Statistical Approach for Differential Expression Analysis of Single-Cell RNA-Seq Data. Genomics, 113 (3), 1308-1324. doi.org/10.1016/j.ygeno.2021.02.014
Series/Report no.: Not Available;
Abstract/Description: Single-cell RNA sequencing (scRNA-seq) is a powerful technology that is capable of generating gene expression data at the resolution of individual cell. The scRNA-seq data is characterized by the presence of dropout events, which severely bias the results if they remain unaddressed. There are limited Differential Expression (DE) approaches which consider the biological processes, which lead to dropout events, in the modeling process. So, we develop, SwarnSeq, an improved method for DE, and other downstream analysis that considers the molecular capture process in scRNA-seq data modeling. The performance of the proposed method is benchmarked with 11 existing methods on 10 different real scRNA-seq datasets under three comparison settings. We demonstrate that SwarnSeq method has improved performance over the 11 existing methods. This improvement is consistently observed across several public scRNA-seq datasets generated using different scRNA-seq protocols. The external spike-ins data can be used in the SwarnSeq method to enhance its performance.
Description: Not Available
ISSN: Not Available
Type(s) of content: Research Paper
Sponsors: Not Available
Language: English
Name of Journal: Genomics
Journal Type: research paper
NAAS Rating: 12.25
Impact Factor: 6.25
Volume No.: 113(3)
Page Number: 1308-1324
Name of the Division/Regional Station: Not Available
Source, DOI or any other URL: doi.org/10.1016/j.ygeno.2021.02.014
URI: http://krishi.icar.gov.in/jspui/handle/123456789/68628
Appears in Collections:AEdu-IASRI-Publication

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