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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/188303
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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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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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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13.070432 |