PT - JOURNAL ARTICLE AU - Jovan Tanevski AU - Thin Nguyen AU - Buu Truong AU - Nikos Karaiskos AU - Mehmet Eren Ahsen AU - Xinyu Zhang AU - Chang Shu AU - Ke Xu AU - Xiaoyu Liang AU - Ying Hu AU - Hoang VV Pham AU - Li Xiaomei AU - Thuc D Le AU - Adi L Tarca AU - Gaurav Bhatti AU - Roberto Romero AU - Nestoras Karathanasis AU - Phillipe Loher AU - Yang Chen AU - Zhengqing Ouyang AU - Disheng Mao AU - Yuping Zhang AU - Maryam Zand AU - Jianhua Ruan AU - Christoph Hafemeister AU - Peng Qiu AU - Duc Tran AU - Tin Nguyen AU - Attila Gabor AU - Thomas Yu AU - Justin Guinney AU - Enrico Glaab AU - Roland Krause AU - Peter Banda AU - DREAM SCTC Consortium AU - Gustavo Stolovitzky AU - Nikolaus Rajewsky AU - Julio Saez-Rodriguez AU - Pablo Meyer TI - Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data AID - 10.26508/lsa.202000867 DP - 2020 Nov 01 TA - Life Science Alliance PG - e202000867 VI - 3 IP - 11 4099 - https://www.life-science-alliance.org/content/3/11/e202000867.short 4100 - https://www.life-science-alliance.org/content/3/11/e202000867.full SO - Life Sci. Alliance2020 Nov 01; 3 AB - Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although 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 zebra fish 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.