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

Dries, Ruben and Zhu, Qian and Dong, Rui and Eng, Chee-Huat Linus and Li, Huipeng and Liu, Kan and Fu, Yuntian and Zhao, Tianxiao and Sarkar, Arpan and Bao, Feng and George, Rani E. and Pierson, Nico and Cai, Long and Yuan, Guo-Cheng (2019) Giotto, a toolbox for integrative analysis and visualization of spatial expression data. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20190716-114136984

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Abstract

The rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/701680DOIDiscussion Paper
ORCID:
AuthorORCID
Cai, Long0000-0002-7154-5361
Yuan, Guo-Cheng0000-0002-2283-4714
Alternate Title:Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. bioRxiv preprint first posted online Jul. 13, 2019.
Record Number:CaltechAUTHORS:20190716-114136984
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190716-114136984
Official Citation:Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data. Ruben Dries, Qian Zhu, Chee-Huat Linus Eng, Arpan Sarkar, Feng Bao, Rani E George, Nico Pierson, Long Cai, Guo-Cheng Yuan. bioRxiv 701680; doi: https://doi.org/10.1101/701680
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97174
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:16 Jul 2019 18:54
Last Modified:01 Jun 2020 15:58

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