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

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 th...

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Detalles Bibliográficos
Autores principales: Alonso, Andrés Mariano, Carrea, Alejandra, Diambra, Luis Aníbal
Formato: Articulo
Lenguaje:Inglés
Publicado: 2020
Materias:
Ciencias Exactas
Single-Cell RNA sequencing
Drosophila Embryo
Gene expression Patterns
DREAM Challenge
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/107899
http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7194340&blobtype=pdf
https://f1000research.com/articles/8-1775/v2
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-107899
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
Single-Cell RNA sequencing
Drosophila Embryo
Gene expression Patterns
DREAM Challenge
spellingShingle Ciencias Exactas
Single-Cell RNA sequencing
Drosophila Embryo
Gene expression Patterns
DREAM Challenge
Alonso, Andrés Mariano
Carrea, Alejandra
Diambra, Luis Aníbal
Prediction of cell position using single-cell transcriptomic data: an iterative procedure
topic_facet Ciencias Exactas
Single-Cell RNA sequencing
Drosophila Embryo
Gene expression Patterns
DREAM Challenge
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.
format Articulo
Articulo
author Alonso, Andrés Mariano
Carrea, Alejandra
Diambra, Luis Aníbal
author_facet Alonso, Andrés Mariano
Carrea, Alejandra
Diambra, Luis Aníbal
author_sort Alonso, Andrés Mariano
title Prediction of cell position using single-cell transcriptomic data: an iterative procedure
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
publishDate 2020
url http://sedici.unlp.edu.ar/handle/10915/107899
http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7194340&blobtype=pdf
https://f1000research.com/articles/8-1775/v2
work_keys_str_mv AT alonsoandresmariano predictionofcellpositionusingsinglecelltranscriptomicdataaniterativeprocedure
AT carreaalejandra predictionofcellpositionusingsinglecelltranscriptomicdataaniterativeprocedure
AT diambraluisanibal predictionofcellpositionusingsinglecelltranscriptomicdataaniterativeprocedure
bdutipo_str Repositorios
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