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.

Details

Title
Giotto: a toolbox for integrative analysis and visualization of spatial expression data
Author
Dries, Ruben; Zhu, Qian; Dong, Rui; Chee-Huat, Linus Eng; Li, Huipeng; Liu, Kan; Fu, Yuntian; Zhao, Tianxiao; Sarkar, Arpan; Bao, Feng; George, Rani E; Pierson, Nico; Long, Cai; Guo-Cheng, Yuan  VIAFID ORCID Logo 
Pages
1-31
Section
Method
Publication year
2021
Publication date
2021
Publisher
Springer Nature B.V.
ISSN
14747596
e-ISSN
1474760X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2502906370
Copyright
© 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.