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

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Item Type:

Article

Title:

Gene selection for optimal prediction of cell position in tissues 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 Guinney, J. 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-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.

Keywords:

Algorithms, Computational Biology, Developmental Gene Expression Regulation, Forecasting, Gene Expression Profiling, Gene Regulatory Networks, Genetic Databases, RNA Sequence Analysis, Single-Cell Analysis, Spatial Analysis, Transcriptome, Animals, Drosophila, Zebrafish

Source:

Life Science Alliance

ISSN:

2575-1077

Publisher:

Life Science Alliance

Volume:

Number:

Page Range:

e202000867

Date:

November 2020