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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/160413
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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 |
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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 |
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info:eu-repo/semantics/altIdentifier/issn/2215-0161 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2022.101778 |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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application/pdf |
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