Cell annotation using scRNA-seq data: a protein-protein interaction network approach

Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify plur...

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Detalles Bibliográficos
Autores: Senra, Daniela, Guisoni, Nara Cristina, Diambra, Luis Aníbal
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:Argentina
Institución:Universidad Nacional de La Plata
Repositorio:SEDICI (UNLP)
Idioma:inglés
OAI Identifier:oai:sedici.unlp.edu.ar:10915/160415
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/160415
Access Level:acceso abierto
Palabra clave:Biología
scRNA-seq
Protein-protein interaction networks
Cell annotation
Biological Processes
Breast cancer
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spelling Cell annotation using scRNA-seq data: a protein-protein interaction network approachSenra, DanielaGuisoni, Nara CristinaDiambra, Luis AníbalBiologíascRNA-seqProtein-protein interaction networksCell annotationBiological ProcessesBreast cancerPathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.Centro Regional de Estudios Genómicos2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/160415enginfo:eu-repo/semantics/altIdentifier/issn/2215-0161info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2023.102179info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2024-05-08T13:20:54Zoai:sedici.unlp.edu.ar:10915/160415Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292024-05-08 13:20:54.364SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Cell annotation using scRNA-seq data: a protein-protein interaction network approach
title Cell annotation using scRNA-seq data: a protein-protein interaction network approach
spellingShingle Cell annotation using scRNA-seq data: a protein-protein interaction network approach
Senra, Daniela
Biología
scRNA-seq
Protein-protein interaction networks
Cell annotation
Biological Processes
Breast cancer
title_short Cell annotation using scRNA-seq data: a protein-protein interaction network approach
title_full Cell annotation using scRNA-seq data: a protein-protein interaction network approach
title_fullStr Cell annotation using scRNA-seq data: a protein-protein interaction network approach
title_full_unstemmed Cell annotation using scRNA-seq data: a protein-protein interaction network approach
title_sort Cell annotation using scRNA-seq data: a protein-protein interaction network approach
dc.creator.none.fl_str_mv Senra, Daniela
Guisoni, Nara Cristina
Diambra, Luis Aníbal
author Senra, Daniela
author_facet Senra, Daniela
Guisoni, Nara Cristina
Diambra, Luis Aníbal
author_role author
author2 Guisoni, Nara Cristina
Diambra, Luis Aníbal
author2_role author
author
dc.subject.none.fl_str_mv Biología
scRNA-seq
Protein-protein interaction networks
Cell annotation
Biological Processes
Breast cancer
topic Biología
scRNA-seq
Protein-protein interaction networks
Cell annotation
Biological Processes
Breast cancer
description Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/160415
url http://sedici.unlp.edu.ar/handle/10915/160415
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2215-0161
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2023.102179
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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