Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types
Summary: Here, we present a computational approach for investigating highly variable genes (HVGs) associated with biological pathways of interest, across multiple time points and cell types in single-cell RNA-sequencing (scRNA-seq) data. Using public dengue virus and COVID-19 datasets, we describe s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | STAR Protocols |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166723003544 |
| _version_ | 1827913815283466240 |
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| author | Jantarika Kumar Arora Anunya Opasawatchai Sarah A. Teichmann Ponpan Matangkasombut Varodom Charoensawan |
| author_facet | Jantarika Kumar Arora Anunya Opasawatchai Sarah A. Teichmann Ponpan Matangkasombut Varodom Charoensawan |
| author_sort | Jantarika Kumar Arora |
| collection | DOAJ |
| description | Summary: Here, we present a computational approach for investigating highly variable genes (HVGs) associated with biological pathways of interest, across multiple time points and cell types in single-cell RNA-sequencing (scRNA-seq) data. Using public dengue virus and COVID-19 datasets, we describe steps for using the framework to characterize the dynamic expression levels of HVGs related to common and cell-type-specific biological pathways over multiple immune cell types.For complete details on the use and execution of this protocol, please refer to Arora et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
| first_indexed | 2024-03-13T02:36:18Z |
| format | Article |
| id | doaj.art-69ad298dc6334e2785c596ecfa093f03 |
| institution | Directory Open Access Journal |
| issn | 2666-1667 |
| language | English |
| last_indexed | 2024-03-13T02:36:18Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj.art-69ad298dc6334e2785c596ecfa093f032023-06-29T04:14:34ZengElsevierSTAR Protocols2666-16672023-09-0143102387Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell typesJantarika Kumar Arora0Anunya Opasawatchai1Sarah A. Teichmann2Ponpan Matangkasombut3Varodom Charoensawan4Doctor of Philosophy Program in Biochemistry (International Program), Faculty of Science, Mahidol University, Bangkok 10400, Thailand; Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, ThailandDepartment of Oral Microbiology, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand; Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom 73170, Thailand; Systems Biology of Diseases Research Unit, Faculty of Science Mahidol University, Bangkok 10400, ThailandWellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Corresponding authorDepartment of Microbiology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; Systems Biology of Diseases Research Unit, Faculty of Science Mahidol University, Bangkok 10400, Thailand; Corresponding authorDepartment of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom 73170, Thailand; Systems Biology of Diseases Research Unit, Faculty of Science Mahidol University, Bangkok 10400, Thailand; School of Chemistry, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand; Corresponding authorSummary: Here, we present a computational approach for investigating highly variable genes (HVGs) associated with biological pathways of interest, across multiple time points and cell types in single-cell RNA-sequencing (scRNA-seq) data. Using public dengue virus and COVID-19 datasets, we describe steps for using the framework to characterize the dynamic expression levels of HVGs related to common and cell-type-specific biological pathways over multiple immune cell types.For complete details on the use and execution of this protocol, please refer to Arora et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.http://www.sciencedirect.com/science/article/pii/S2666166723003544BioinformaticsSingle CellRNAseqImmunologyGene ExpressionSystems Biology |
| spellingShingle | Jantarika Kumar Arora Anunya Opasawatchai Sarah A. Teichmann Ponpan Matangkasombut Varodom Charoensawan Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types STAR Protocols Bioinformatics Single Cell RNAseq Immunology Gene Expression Systems Biology |
| title | Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types |
| title_full | Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types |
| title_fullStr | Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types |
| title_full_unstemmed | Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types |
| title_short | Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types |
| title_sort | computational workflow for investigating highly variable genes in single cell rna seq across multiple time points and cell types |
| topic | Bioinformatics Single Cell RNAseq Immunology Gene Expression Systems Biology |
| url | http://www.sciencedirect.com/science/article/pii/S2666166723003544 |
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