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

被引：2

作者：

Arora, Jantarika Kumar ^{[1
,2
]}

Opasawatchai, Anunya ^{[3
,4
,7
]}

Teichmann, Sarah A. ^{[5
]}

Matangkasombut, Ponpan ^{[6
,7
]}

Charoensawan, Varodom ^{[2
,4
,7
,8
]}

机构：

[1] Mahidol Univ, Fac Sci, Philosophy Program Biochem, Int Program, Bangkok 10400, Thailand

[2] Mahidol Univ, Fac Sci, Dept Biochem, Bangkok 10400, Thailand

[3] Mahidol Univ, Fac Dent, Dept Oral Microbiol, Bangkok 10400, Thailand

[4] Mahidol Univ, Integrat Computat Biosci ICBS Ctr, Nakhon Pathom 73170, Thailand

[5] Wellcome Sanger Inst, Wellcome Trust Genome Campus, Cambridge CB10 1SA, England

[6] Mahidol Univ, Fac Sci, Dept Microbiol, Bangkok 10400, Thailand

[7] Fac Sci Mahidol Univ, Syst Biol Dis Res Unit, Fac Sci, Bangkok 10400, Thailand

[8] Suranaree Univ Technol, Inst Sci, Sch Chem, Nakhon Ratchasima 30000, Thailand

来源：

STAR PROTOCOLS | 2023年 / 4卷 / 03期

基金：

英国医学研究理事会;

关键词：

DIFFERENTIAL EXPRESSION ANALYSIS;

D O I：

10.1016/j.xpro.2023.102387

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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

引用

页数：20

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