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

Autores
Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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. • We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes. • The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.
Fil: Senra, Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina
Fil: Guisoni, Nara Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina
Fil: Diambra, Luis Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina
Materia
BIOLOGICAL PROCESSES
BREAST CANCER
CELL ANNOTATION
PROTEIN-PROTEIN INTERACTION NETWORKS
SCRNA-SEQ
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/221884
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/221884
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Cell annotation using scRNA-seq data: A protein-protein interaction network approachSenra, DanielaGuisoni, Nara CristinaDiambra, Luis AnibalBIOLOGICAL PROCESSESBREAST CANCERCELL ANNOTATIONPROTEIN-PROTEIN INTERACTION NETWORKSSCRNA-SEQhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Pathway 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. • We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes. • The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.Fil: Senra, Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; ArgentinaFil: Guisoni, Nara Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; ArgentinaFil: Diambra, Luis Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; ArgentinaElsevier2023-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/221884Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Cell annotation using scRNA-seq data: A protein-protein interaction network approach; Elsevier; MethodsX; 10; 102179; 4-2023; 1-82215-0161CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2215016123001796info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2023.102179info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:37:46Zoai:ri.conicet.gov.ar:11336/221884instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:37:47.067CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
BIOLOGICAL PROCESSES
BREAST CANCER
CELL ANNOTATION
PROTEIN-PROTEIN INTERACTION NETWORKS
SCRNA-SEQ
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 Anibal
author Senra, Daniela
author_facet Senra, Daniela
Guisoni, Nara Cristina
Diambra, Luis Anibal
author_role author
author2 Guisoni, Nara Cristina
Diambra, Luis Anibal
author2_role author
author
dc.subject.none.fl_str_mv BIOLOGICAL PROCESSES
BREAST CANCER
CELL ANNOTATION
PROTEIN-PROTEIN INTERACTION NETWORKS
SCRNA-SEQ
topic BIOLOGICAL PROCESSES
BREAST CANCER
CELL ANNOTATION
PROTEIN-PROTEIN INTERACTION NETWORKS
SCRNA-SEQ
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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. • We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes. • The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.
Fil: Senra, Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina
Fil: Guisoni, Nara Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina
Fil: Diambra, Luis Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina
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. • We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes. • The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.
publishDate 2023
dc.date.none.fl_str_mv 2023-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/221884
Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Cell annotation using scRNA-seq data: A protein-protein interaction network approach; Elsevier; MethodsX; 10; 102179; 4-2023; 1-8
2215-0161
CONICET Digital
CONICET
url http://hdl.handle.net/11336/221884
identifier_str_mv Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Cell annotation using scRNA-seq data: A protein-protein interaction network approach; Elsevier; MethodsX; 10; 102179; 4-2023; 1-8
2215-0161
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2215016123001796
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2023.102179
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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