ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data

Autores
Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Aníbal
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set. •ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package. •ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.
Centro Regional de Estudios Genómicos
Materia
Biología
Stem cells
scRNA-seq
Protein-protein interaction networks
Trajectory inference
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/160413

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network_name_str SEDICI (UNLP)
spelling ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq dataSenra, DanielaGuisoni, Nara CristinaDiambra, Luis AníbalBiologíaStem cellsscRNA-seqProtein-protein interaction networksTrajectory inferenceTrajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set. •ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package. •ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.Centro Regional de Estudios Genómicos2022info: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/10915/160413enginfo:eu-repo/semantics/altIdentifier/issn/2215-0161info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2022.101778info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:42:01Zoai:sedici.unlp.edu.ar:10915/160413Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:42:01.512SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
title ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
spellingShingle ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
Senra, Daniela
Biología
Stem cells
scRNA-seq
Protein-protein interaction networks
Trajectory inference
title_short ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
title_full ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
title_fullStr ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
title_full_unstemmed ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
title_sort ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
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
Stem cells
scRNA-seq
Protein-protein interaction networks
Trajectory inference
topic Biología
Stem cells
scRNA-seq
Protein-protein interaction networks
Trajectory inference
dc.description.none.fl_txt_mv Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set. •ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package. •ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.
Centro Regional de Estudios Genómicos
description Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set. •ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package. •ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.
publishDate 2022
dc.date.none.fl_str_mv 2022
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/10915/160413
url http://sedici.unlp.edu.ar/handle/10915/160413
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.2022.101778
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 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
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
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institution 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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