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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Bibliographic Details
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
Description
Summary: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.
ISSN:2046-1402