The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge.
IF: 13.994
Cited by: 1


Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data. We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.


Spatial reconstruction
DREAM challenge
cellular position prediction
single-cell transcriptomics

MeSH terms

Computational Biology
Sequence Analysis, RNA
Single-Cell Analysis


Pham, Vu V H
Li, Xiaomei
Truong, Buu
Nguyen, Thin
Liu, Lin
Li, Jiuyong
Le, Thuc D

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