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

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

Mahidol University
Wellcome Trust Sanger Institute
Suranaree University of Technology

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

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

Original language	English
Article number	102387
Journal	STAR Protocols
Volume	4
Issue number	3
DOIs	https://doi.org/10.1016/j.xpro.2023.102387
Publication status	Published - 15 Sept 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bioinformatics
Gene Expression
Immunology
RNAseq
Single Cell
Systems Biology

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10.1016/j.xpro.2023.102387

Cite this

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title = "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",

keywords = "Bioinformatics, Gene Expression, Immunology, RNAseq, Single Cell, Systems Biology",

author = "Arora, \{Jantarika Kumar\} and Anunya Opasawatchai and Teichmann, \{Sarah A.\} and Ponpan Matangkasombut and Varodom Charoensawan",

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doi = "10.1016/j.xpro.2023.102387",

language = "English",

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AU - Arora, Jantarika Kumar

AU - Opasawatchai, Anunya

AU - Teichmann, Sarah A.

AU - Matangkasombut, Ponpan

AU - Charoensawan, Varodom

PY - 2023/9/15

Y1 - 2023/9/15

N2 - 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

AB - 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

KW - Bioinformatics

KW - Gene Expression

KW - Immunology

KW - RNAseq

KW - Single Cell

KW - Systems Biology

UR - https://www.scopus.com/pages/publications/85163519797

U2 - 10.1016/j.xpro.2023.102387

DO - 10.1016/j.xpro.2023.102387

M3 - Article

C2 - 37379219

AN - SCOPUS:85163519797

SN - 2666-1667

VL - 4

JO - STAR Protocols

JF - STAR Protocols

IS - 3

M1 - 102387

ER -

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

Abstract

UN SDGs

Keywords

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