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

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.

Highlights

Single-cell sequencing reveals cellular heterogeneity but not cell localization
In this paper we present a procedure for solving the cell-position problem posed in the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Single Cell Transcriptomics Challenge (SCTC)
DREAM challenge data Expression patterns used as a reference atlas correspond to 84 driver genes obtained from in situ hybridization experiments; the data correspond to The Berkeley Drosophila Transcription Network Project (BDTNP)[8]

Summary

Introduction

Single-cell sequencing reveals cellular heterogeneity but not cell localization. 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

Methods

Results

Conclusion