Cell annotation using scRNA-seq data: a protein-protein interaction network approach
- Autores
- Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Aníbal
- 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.
Centro Regional de Estudios Genómicos - Materia
-
Biología
scRNA-seq
Protein-protein interaction networks
Cell annotation
Biological Processes
Breast cancer - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-nd/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/160415
Ver los metadatos del registro completo
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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:UNLP2025-09-29T11:42:01Zoai: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:13292025-09-29 11:42:01.514SEDICI (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 |
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. Centro Regional de Estudios Genómicos |
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 |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) |
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