Authors: Jovan Tanevski, Thanh Nguyen, Buu Truong, Nikos Karaiskos, Mehmet Eren Ahsen, Xuan Zhang, Chang Shu, K e Xu, Xiaoyu Liang, Ying Hu, Pham Hv, Xiaomei L, Thuc Duy Le, Adi L. Tarca, Gaurav Bhatti, Roberto Romero, Nestoras Karathanasis, Phillipe Loher, Yang Chen, Zhengqing Ouyang, Disheng Mao, Yuping Zhang, Maryam Zand, Jianhua Ruan, Christoph Hafemeister, Peng Qiu, Duc A. Tran, Attila Gábor, Thomas Yu, Enrico Glaab, Roland Krause, Peter Banda, Gustavo Stolovitzky, Nikolaus Rajewsky, Julio Saez-Rodriguez, Pablo Meyer
Venue: N/A
Type: Publication
Abstract: Abstract Single-cell RNA-seq (scRNAseq) technologies are rapidly evolving and a growing number of datasets are now available. While very informative, in standard scRNAseq experiments the spatial organization of the cells in the organism or 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 data of genes with spatial information can thus increase coverage while providing spatial location. However, methods to perform such ...
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Topics: 
Computational biology
Genetics
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