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.