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

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 th...

Descripción completa

Detalles Bibliográficos
Autores principales: Tanevski, Jovan, Nguyen, Thin, Truong, Buu, Karaiskos, Nikos, Ahsen, Mehmet Eren, Zhang, Xinyu, Shu, Chang, Xu, Ke, Liang, Xiaoyu, Hu, Ying, Pham, Hoang VV, Xiaomei, Li, Le, Thuc D, Tarca, Adi L, Bhatti, Gaurav, Romero, Roberto, Karathanasis, Nestoras, Loher, Phillipe, Chen, Yang, Ouyang, Zhengqing, Mao, Disheng, Zhang, Yuping, Zand, Maryam, Ruan, Jianhua, Hafemeister, Christoph, Qiu, Peng, Tran, Duc, Nguyen, Tin, Gabor, Attila, Yu, Thomas, Guinney, Justin, Glaab, Enrico, Krause, Roland, Banda, Peter, Stolovitzky, Gustavo, Rajewsky, Nikolaus, Saez-Rodriguez, Julio, Meyer, Pablo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Life Science Alliance LLC 2020
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536825/
https://www.ncbi.nlm.nih.gov/pubmed/32972997
http://dx.doi.org/10.26508/lsa.202000867

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536825/
https://www.ncbi.nlm.nih.gov/pubmed/32972997
http://dx.doi.org/10.26508/lsa.202000867

Ejemplares similares