Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics

Single-cell RNA-seq (scRNAseq) technologies are rapidly evolving. While very
informative, in standard scRNAseq experiments the spatial organization of the cells in the
tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell
localization have limited throughput and gene coverage. Mapping scRNAseq to genes with
spatial information increases coverage while providing spatial location. However, methods
to perform such mapping have not yet been benchmarked. To fill this gap, we organized
the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction
of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard,
genes with in situ hybridization data from the Berkeley Drosophila Transcription Network
Project reference atlas. The 34 participating teams used diverse algorithms for gene
selection and location prediction, while being able to correctly localize clusters of cells.
Selection of predictor genes was essential for this task. Predictor genes showed a relatively
high expression entropy, high spatial clustering and included prominent developmental
genes such as gap and pair-rule genes and tissue markers. Application of the Top-10
methods to a zebrafish embryo dataset yielded similar performance and statistical
properties of the selected genes than in the Drosophila data. This suggests that methods
developed in this challenge are able to extract generalizable properties of genes that are
useful to accurately reconstruct the spatial arrangement of cells in tissues.