Publication:
Analyzing single cell transcriptome data from severe COVID-19 patients

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dc.contributor.authorNassir, Nasna
dc.contributor.authorTambi, Richa
dc.contributor.authorBankapur, Asma
dc.contributor.authorKaruvantevida, Noushad
dc.contributor.authorZehra, Binte
dc.contributor.authorBegum, Ghausia
dc.contributor.authorHameid, Reem Abdel
dc.contributor.authorAhmed, Awab
dc.contributor.authorShabestari, Seyed Ali Safizadeh
dc.contributor.authorHachim, Mahmood Yaseen
dc.contributor.authorAlsheikh-Ali, Alawi
dc.contributor.authorBerdiev, Bakhrom
dc.contributor.authorAl Heialy, Saba
dc.contributor.authorUddin, Mohammed
dc.date.accessioned2023-03-13T04:25:07Z
dc.date.available2023-03-13T04:25:07Z
dc.date.issued2022
dc.description.abstractSUMMARY: We describe the protocol for identifying COVID-19 severity specific cell types and their regulatory marker genes using single-cell transcriptomics data. We construct COVID-19 comorbid disease-associated gene list using multiple databases and literature resources. Next, we identify specific cell type where comorbid genes are upregulated. We further characterize the identified cell type using gene enrichment analysis. We detect upregulation of marker gene restricted to severe COVID-19 cell type and validate our findings using in silico, in vivo, and in vitro cellular models.en_US
dc.identifier.other204-2022.58
dc.identifier.urihttps://repository.mbru.ac.ae/handle/1/1078
dc.language.isoenen_US
dc.subjectCOVID-19en_US
dc.subjectPatientsen_US
dc.subjectDataen_US
dc.subjectData clustering and analysisen_US
dc.subjectSingle cell transcriptomeen_US
dc.titleAnalyzing single cell transcriptome data from severe COVID-19 patientsen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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