RT Journal Article SR Electronic T1 Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data JF Life Science Alliance JO Life Sci. Alliance FD Life Science Alliance LLC SP e202000867 DO 10.26508/lsa.202000867 VO 3 IS 11 A1 Tanevski, Jovan A1 Nguyen, Thin A1 Truong, Buu A1 Karaiskos, Nikos A1 Ahsen, Mehmet Eren A1 Zhang, Xinyu A1 Shu, Chang A1 Xu, Ke A1 Liang, Xiaoyu A1 Hu, Ying A1 Pham, Hoang VV A1 Xiaomei, Li A1 Le, Thuc D A1 Tarca, Adi L A1 Bhatti, Gaurav A1 Romero, Roberto A1 Karathanasis, Nestoras A1 Loher, Phillipe A1 Chen, Yang A1 Ouyang, Zhengqing A1 Mao, Disheng A1 Zhang, Yuping A1 Zand, Maryam A1 Ruan, Jianhua A1 Hafemeister, Christoph A1 Qiu, Peng A1 Tran, Duc A1 Nguyen, Tin A1 Gabor, Attila A1 Yu, Thomas A1 Guinney, Justin A1 Glaab, Enrico A1 Krause, Roland A1 Banda, Peter A1 , A1 Stolovitzky, Gustavo A1 Rajewsky, Nikolaus A1 Saez-Rodriguez, Julio A1 Meyer, Pablo YR 2020 UL http://www.life-science-alliance.org/content/3/11/e202000867.abstract 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.