The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge

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.

Researchers from the University of South Australia 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, the researchers present an R package, SCTCwhatateam, which includes all the methods they developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction.

Workflow of SCTCwhatateam

SCTCwhatateam has three components, including data preprocessing, gene selection, and prediction. Each component has multiple methods and each pipeline is a combination of a method in each component.