Giotto: a toolbox for integrative analysis and visualization of spatial expression data

Ruben Dries(Boston University), Qian Zhu(Harvard University), Rui Dong(Harvard University), Chee-Huat Linus Eng(California Institute of Technology), Huipeng Li(Harvard University), Kan Liu(Tsinghua University), Yuntian Fu(Harvard University), Tianxiao Zhao(Harvard University), Arpan Sarkar(Harvard University), Feng Bao(Tsinghua University), Rani E. George(Harvard University), Nico Pierson(California Institute of Technology), Long Cai(California Institute of Technology), Guo‐Cheng Yuan(Dana-Farber Cancer Institute)
Genome biology
March 8, 2021
Cited by 936Open Access
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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.


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