| Literature DB >> 33685491 |
Ruben Dries1,2, Qian Zhu3, Rui Dong3, Chee-Huat Linus Eng4, Huipeng Li3, Kan Liu5, Yuntian Fu3, Tianxiao Zhao3, Arpan Sarkar3,6, Feng Bao5, Rani E George3, Nico Pierson4, Long Cai4, Guo-Cheng Yuan7,8,9.
Abstract
Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.Entities:
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Year: 2021 PMID: 33685491 PMCID: PMC7938609 DOI: 10.1186/s13059-021-02286-2
Source DB: PubMed Journal: Genome Biol ISSN: 1474-7596 Impact factor: 17.906