Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types

Jantarika Kumar Arora; Anunya Opasawatchai; Sarah A Teichmann; Ponpan Matangkasombut; Varodom Charoensawan

doi:10.1016/j.xpro.2023.102387

Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types

STAR Protoc. 2023 Sep 15;4(3):102387. doi: 10.1016/j.xpro.2023.102387. Epub 2023 Jun 27.

Authors

Jantarika Kumar Arora¹, Anunya Opasawatchai², Sarah A Teichmann³, Ponpan Matangkasombut⁴, Varodom Charoensawan⁵

Affiliations

¹ Doctor 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, Thailand.
² Department 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, Thailand.
³ Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. Electronic address: st9@sanger.ac.uk.
⁴ Department of Microbiology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand; Systems Biology of Diseases Research Unit, Faculty of Science Mahidol University, Bangkok 10400, Thailand. Electronic address: ponpan.mat@mahidol.edu.
⁵ Department 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. Electronic address: varodom.cha@mahidol.ac.th.

Abstract

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.

Keywords: Bioinformatics; Gene Expression; Immunology; RNAseq; Single Cell; Systems Biology.

MeSH terms

Gene Expression Profiling* / methods
Sequence Analysis, RNA / methods
Single-Cell Analysis / methods
Single-Cell Gene Expression Analysis*
Workflow