%0 Journal Article %A Jovan Tanevski %A Thin Nguyen %A Buu Truong %A Nikos Karaiskos %A Mehmet Eren Ahsen %A Xinyu Zhang %A Chang Shu %A Ke Xu %A Xiaoyu Liang %A Ying Hu %A Hoang V.V. Pham %A Li Xiaomei %A Thuc D. Le %A Adi L. Tarca %A Gaurav Bhatti %A Roberto Romero %A Nestoras Karathanasis %A Phillipe Loher %A Yang Chen %A Zhengqing Ouyang %A Disheng Mao %A Yuping Zhang %A Maryam Zand %A Jianhua Ruan %A Christoph Hafemeister %A Peng Qiu %A Duc Tran %A Tin Nguyen %A Attila Gabor %A Thomas Yu %A Enrico Glaab %A Roland Krause %A Peter Banda %A DREAM SCTC Consortium %A Gustavo Stolovitzky %A Nikolaus Rajewsky %A Julio Saez-Rodriguez %A Pablo Meyer %T Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data %D 2019 %R 10.1101/796029 %J bioRxiv %P 796029 %X Single-cell RNA-seq technologies are rapidly evolving but 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 keep the localization of the cells 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 bridge the 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 gold 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 rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers. %U https://www.biorxiv.org/content/biorxiv/early/2019/10/10/796029.1.full.pdf