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

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 appr...

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Autores principales: Senra, Daniela, Guisoni, Nara Cristina, Diambra, Luis Aníbal
Formato: Articulo
Lenguaje:Inglés
Publicado: 2022
Materias:
Biología
Stem cells
scRNA-seq
Protein-protein interaction networks
Trajectory inference
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/160413
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-160413
record_format dspace
spelling I19-R120-10915-1604132023-11-22T20:06:58Z http://sedici.unlp.edu.ar/handle/10915/160413 ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data Senra, Daniela Guisoni, Nara Cristina Diambra, Luis Aníbal 2022 2023-11-22T16:29:46Z en Biología Stem cells scRNA-seq Protein-protein interaction networks Trajectory inference 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 Articulo Articulo http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) application/pdf
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Biología
Stem cells
scRNA-seq
Protein-protein interaction networks
Trajectory inference
spellingShingle Biología
Stem cells
scRNA-seq
Protein-protein interaction networks
Trajectory inference
Senra, Daniela
Guisoni, Nara Cristina
Diambra, Luis Aníbal
ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
topic_facet Biología
Stem cells
scRNA-seq
Protein-protein interaction networks
Trajectory inference
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.
format Articulo
Articulo
author Senra, Daniela
Guisoni, Nara Cristina
Diambra, Luis Aníbal
author_facet Senra, Daniela
Guisoni, Nara Cristina
Diambra, Luis Aníbal
author_sort Senra, Daniela
title ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data
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
publishDate 2022
url http://sedici.unlp.edu.ar/handle/10915/160413
work_keys_str_mv AT senradaniela originsaproteinnetworkbasedapproachtoquantifycellpluripotencyfromscrnaseqdata
AT guisoninaracristina originsaproteinnetworkbasedapproachtoquantifycellpluripotencyfromscrnaseqdata
AT diambraluisanibal originsaproteinnetworkbasedapproachtoquantifycellpluripotencyfromscrnaseqdata
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