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

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.

SUBMITTER: Arora JK

PROVIDER: S-EPMC10331590 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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

Arora Jantarika Kumar JK Opasawatchai Anunya A Teichmann Sarah A SA Matangkasombut Ponpan P Charoensawan Varodom V

STAR protocols 20230627 3

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 ...[more]

PMID: 37379219

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