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

Autores
Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal
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.
Fil: Senra, Daniela. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Guisoni, Nara Cristina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Materia
ORIGINS
PROTEIN-PROTEIN INTERACTION NETWORKS
SCRNA-SEQ
STEM CELLS
TRAJECTORY INFERENCE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/188303

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spelling Origins: A protein network-based approach to quantify cell pluripotency from scRNA-seq dataSenra, DanielaGuisoni, Nara CristinaDiambra, Luis AnibalORIGINSPROTEIN-PROTEIN INTERACTION NETWORKSSCRNA-SEQSTEM CELLSTRAJECTORY INFERENCEhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Trajectory 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.Fil: Senra, Daniela. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Guisoni, Nara Cristina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaElsevier2022-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/188303Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Origins: A protein network-based approach to quantify cell pluripotency from scRNA-seq data; Elsevier; MethodsX; 9; 101778; 1-2022; 1-122215-0161CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2022.101778info:eu-repo/semantics/altIdentifier/url/https://methods-x.com/article/S2215-0161(22)00158-3/fulltextinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:56:38Zoai:ri.conicet.gov.ar:11336/188303instacron: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 09:56:39.241CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
ORIGINS
PROTEIN-PROTEIN INTERACTION NETWORKS
SCRNA-SEQ
STEM CELLS
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 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 ORIGINS
PROTEIN-PROTEIN INTERACTION NETWORKS
SCRNA-SEQ
STEM CELLS
TRAJECTORY INFERENCE
topic ORIGINS
PROTEIN-PROTEIN INTERACTION NETWORKS
SCRNA-SEQ
STEM CELLS
TRAJECTORY INFERENCE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
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.
Fil: Senra, Daniela. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Guisoni, Nara Cristina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
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 2022-01
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/11336/188303
Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Origins: A protein network-based approach to quantify cell pluripotency from scRNA-seq data; Elsevier; MethodsX; 9; 101778; 1-2022; 1-12
2215-0161
CONICET Digital
CONICET
url http://hdl.handle.net/11336/188303
identifier_str_mv Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Origins: A protein network-based approach to quantify cell pluripotency from scRNA-seq data; Elsevier; MethodsX; 9; 101778; 1-2022; 1-12
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/doi/10.1016/j.mex.2022.101778
info:eu-repo/semantics/altIdentifier/url/https://methods-x.com/article/S2215-0161(22)00158-3/fulltext
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
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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