Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]

Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]

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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Main Authors: Andrés M. Alonso, Alejandra Carrea, Luis Diambra
Format: Article
Language:English
Published: F1000 Research Ltd 2020-04-01
Series:F1000Research
Online Access:https://f1000research.com/articles/8-1775/v2
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spelling doaj-94dea366edd1483e9aad90ee810a7e392020-11-25T02:33:32ZengF1000 Research LtdF1000Research2046-14022020-04-01810.12688/f1000research.20715.225587Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]Andrés M. Alonso0Alejandra Carrea1Luis Diambra2CREG-CONICET, Universidad Nacional de La Plata, La Plata, Buenos Aires, 1900, ArgentinaCREG-CONICET, Universidad Nacional de La Plata, La Plata, Buenos Aires, 1900, ArgentinaCREG-CONICET, Universidad Nacional de La Plata, La Plata, Buenos Aires, 1900, ArgentinaSingle-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.https://f1000research.com/articles/8-1775/v2
collection DOAJ
language English
format Article
sources DOAJ
author Andrés M. Alonso
Alejandra Carrea
Luis Diambra
spellingShingle Andrés M. Alonso
Alejandra Carrea
Luis Diambra
Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]
F1000Research
author_facet Andrés M. Alonso
Alejandra Carrea
Luis Diambra
author_sort Andrés M. Alonso
title Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]
title_short Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]
title_full Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]
title_fullStr Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]
title_full_unstemmed Prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]
title_sort prediction of cell position using single-cell transcriptomic data: an iterative procedure [version 2; peer review: 2 approved]
publisher F1000 Research Ltd
series F1000Research
issn 2046-1402
publishDate 2020-04-01
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.
url https://f1000research.com/articles/8-1775/v2
work_keys_str_mv AT andresmalonso predictionofcellpositionusingsinglecelltranscriptomicdataaniterativeprocedureversion2peerreview2approved
AT alejandracarrea predictionofcellpositionusingsinglecelltranscriptomicdataaniterativeprocedureversion2peerreview2approved
AT luisdiambra predictionofcellpositionusingsinglecelltranscriptomicdataaniterativeprocedureversion2peerreview2approved
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