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Predicting cellular position in the Drosophila embryo from single-cell Transcriptomics data

Item Type:Preprint
Title:Predicting cellular position in the Drosophila embryo from single-cell Transcriptomics data
Creators Name:Tanevski, J. and Nguyen, T. and Truong, B. and Karaiskos, N. and Ahsen, M.E. and Zhang, X. and Shu, C. and Xu, K. and Liang, X. and Hu, Y. and Pham, H.V.V. and Xiaomei, L. and Le, T.D. and Tarca, A.L. and Bhatti, G. and Romero, R. and Karathanasis, N. and Loher, P. and Chen, Y. and Ouyang, Z. and Mao, D. and Zhang, Y. and Zand, M. and Ruan, J. and Hafemeister, C. and Qiu, P. and Tran, D. and Nguyen, T. and Gabor, A. and Yu, T. and Glaab, E. and Krause, R. and Banda, P. and Stolovitzky, G. and Rajewsky, N. and Saez-Rodriguez, J. and Meyer, P.
Abstract: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.
Publisher:Cold Spring Harbor Laboratory Press
Article Number:796029
Date:10 October 2019
Official Publication:https://doi.org/10.1101/796029

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