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Please use this identifier to cite or link to this item: http://krishi.icar.gov.in/jspui/handle/123456789/68626
Title: Analysis of Single-Cell RNA-seq Data from Adenocarcinoma Cell Lines: A Stepwise Guide.
Other Titles: Not Available
Authors: A. Malhotra
Samarendra Das
S.N. Rai
ICAR Data Use Licennce: http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf
Author's Affiliated institute: University of Louisville, USA
ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
Published/ Complete Date: 2022-01-01
Project Code: Not Available
Keywords: scRNA-seq
clustering
differential expression
comparative analysis
negative binomial
zero-inflated negative binomial
ROC curve
AUC
Publisher: MDPI Publisher
Citation: Malhotra, A., Das, S. and Rai, S.N. (2022). Analysis of Single-Cell RNA-seq Data from Adenocarcinoma Cell Lines: A Stepwise Guide. BioMedInformatics. 2(1), 43-61. doi.org/10.3390/biomedinformatics2010003
Series/Report no.: Not Available;
Abstract/Description: Single-cell RNA-sequencing (scRNA-seq) technology provides an excellent platform for measuring the expression profiles of genes in heterogeneous cell populations. Multiple tools for the analysis of scRNA-seq data have been developed over the years. The tools require complicated commands and steps to analyze the underlying data, which are not easy to follow by genome researchers and experimental biologists. Therefore, we describe a step-by-step workflow for processing and analyzing the scRNA-seq unique molecular identifier (UMI) data from Human Lung Adenocarcinoma cell lines. We demonstrate the basic analyses including quality check, mapping and quantification of transcript abundance through suitable real data example to obtain UMI count data. Further, we performed basic statistical analyses, such as zero-inflation, differential expression and clustering analyses on the obtained count data. We studied the effects of excess zero-inflation present in scRNA-seq data on the downstream analyses. Our findings indicate that the zero-inflation associated with UMI data had no or minimal role in clustering, while it had significant effect on identifying differentially expressed genes. We also provide an insight into the comparative analysis for differential expression analysis tools based on zero-inflated negative binomial and negative binomial models on scRNA-seq data. The sensitivity analysis enhanced our findings in that the negative binomial model-based tool did not provide an accurate and efficient way to analyze the scRNA-seq data. This study provides a set of guidelines for the users to handle and analyze real scRNA-seq data more easily.
Description: Not Available
ISSN: Not Available
Type(s) of content: Research Paper
Sponsors: This study was supported by Netaji Subhas-ICAR International Fellowship, OM No. 18(02)/2016-EQR/Edn. (S.D.) of Indian Council of Agricultural Research (ICAR), New Delhi, India. It was also partly supported by Wendell Cherry Chair (S.N.R.) in Clinical Trial Research, University of Louisville, USA., and multiple National Institutes of Health (NIH) grants (5P20GM113226, PI: McClain; 1P42ES023716, PI: Srivastava; 5P30GM127607-02, PI: Jones; 1P20GM125504-01, PI: Lamont; 2U54HL120163, PI: Bhatnagar/Robertson; 1P20GM135004, PI: Yan; 1R35ES0238373-01, PI: Cave; 1R01ES029846, PI: Bhatnagar; 1R01ES027778-01A1, PI: States) and Kentucky Council on Postsecondary Education grant (PON2 415 1900002934, PI: Chesney). The content is solely the responsibility of the authors and does not necessarily represent the views of NIH or ICAR.
Language: English
Name of Journal: BioMedInformatics
Journal Type: research paper
Volume No.: 2(1)
Page Number: 43-61
Name of the Division/Regional Station: Not Available
Source, DOI or any other URL: doi.org/10.3390/biomedinformatics2010003
URI: http://krishi.icar.gov.in/jspui/handle/123456789/68626
Appears in Collections:AEdu-IASRI-Publication

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