Prediction of cell position using single-cell transcriptomic data: an iterative procedure

Autores
Alonso, Andrés Mariano; Carrea, Alejandra; Diambra, Luis Aníbal
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.
Facultad de Ciencias Exactas
Centro Regional de Estudios Genómicos
Materia
Ciencias Exactas
Single-Cell RNA sequencing
Drosophila Embryo
Gene expression Patterns
DREAM Challenge
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:10915/107899
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Prediction of cell position using single-cell transcriptomic data: an iterative procedureAlonso, Andrés MarianoCarrea, AlejandraDiambra, Luis AníbalCiencias ExactasSingle-Cell RNA sequencingDrosophila EmbryoGene expression PatternsDREAM ChallengeSingle-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.Facultad de Ciencias ExactasCentro Regional de Estudios Genómicos2020info: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/10915/107899enginfo:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7194340&blobtype=pdfinfo:eu-repo/semantics/altIdentifier/url/https://f1000research.com/articles/8-1775/v2info:eu-repo/semantics/altIdentifier/issn/2046-1402info:eu-repo/semantics/altIdentifier/pmid/32399185info:eu-repo/semantics/altIdentifier/doi/10.12688/f1000research.20715.2info: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:UNLP2025-09-29T11:23:51Zoai:sedici.unlp.edu.ar:10915/107899Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:23:51.981SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Prediction of cell position using single-cell transcriptomic data: an iterative procedure
title Prediction of cell position using single-cell transcriptomic data: an iterative procedure
spellingShingle Prediction of cell position using single-cell transcriptomic data: an iterative procedure
Alonso, Andrés Mariano
Ciencias Exactas
Single-Cell RNA sequencing
Drosophila Embryo
Gene expression Patterns
DREAM Challenge
title_short Prediction of cell position using single-cell transcriptomic data: an iterative procedure
title_full Prediction of cell position using single-cell transcriptomic data: an iterative procedure
title_fullStr Prediction of cell position using single-cell transcriptomic data: an iterative procedure
title_full_unstemmed Prediction of cell position using single-cell transcriptomic data: an iterative procedure
title_sort Prediction of cell position using single-cell transcriptomic data: an iterative procedure
dc.creator.none.fl_str_mv Alonso, Andrés Mariano
Carrea, Alejandra
Diambra, Luis Aníbal
author Alonso, Andrés Mariano
author_facet Alonso, Andrés Mariano
Carrea, Alejandra
Diambra, Luis Aníbal
author_role author
author2 Carrea, Alejandra
Diambra, Luis Aníbal
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Exactas
Single-Cell RNA sequencing
Drosophila Embryo
Gene expression Patterns
DREAM Challenge
topic Ciencias Exactas
Single-Cell RNA sequencing
Drosophila Embryo
Gene expression Patterns
DREAM Challenge
dc.description.none.fl_txt_mv Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.
Facultad de Ciencias Exactas
Centro Regional de Estudios Genómicos
description Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.
publishDate 2020
dc.date.none.fl_str_mv 2020
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info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/107899
url http://sedici.unlp.edu.ar/handle/10915/107899
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/url/https://f1000research.com/articles/8-1775/v2
info:eu-repo/semantics/altIdentifier/issn/2046-1402
info:eu-repo/semantics/altIdentifier/pmid/32399185
info:eu-repo/semantics/altIdentifier/doi/10.12688/f1000research.20715.2
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
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