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

Andrés M Alonso; Alejandra Carrea; Luis Diambra

doi:10.12688/f1000research.20715.2

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

F1000Res. 2019 Oct 18:8:1775. doi: 10.12688/f1000research.20715.2. eCollection 2019.

Authors

Andrés M Alonso^{1

2}, Alejandra Carrea¹, Luis Diambra¹

Affiliations

¹ CREG-CONICET, Universidad Nacional de La Plata, La Plata, Buenos Aires, 1900, Argentina.
² INTech-CONICET, Universidad Nacional de San Martin, Chascomus, Buenos Aires, Argentina.

Abstract

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.

Keywords: DREAM Challenge; Drosophila Embryo; Gene expression Patterns; Single-Cell RNA sequencing.

MeSH terms

Algorithms*
Computational Biology
Gene Expression Profiling*
Single-Cell Analysis
Transcriptome*

Grants and funding

The author(s) declared that no grants were involved in supporting this work.