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Published March 8, 2021 | Supplemental Material + Submitted + Published
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Giotto: a toolbox for integrative analysis and visualization of spatial expression data

Dries, Ruben
Zhu, Qian
Dong, Rui
Eng, Chee-Huat Linus
Li, Huipeng
Liu, Kan
Fu, Yuntian
Zhao, Tianxiao
Sarkar, Arpan
Bao, Feng
George, Rani E.
Pierson, Nico ORCID icon
Cai, Long ORCID icon
Yuan, Guo-Cheng ORCID icon
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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.

Additional Information

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Received 16 December 2020; Accepted 01 February 2021; Published 08 March 2021. This research was supported by NIH grants UH3HL145609 and R01AG066028 to L.C. and G.Y. Availability of data and materials: General information, examples, and tutorials are available at http://www.spatialgiotto.com. Users can follow to replicate the analyses in this paper and to learn more about the parameters of specific functions in the Giotto package. Giotto v1.0.2 was used for generating the analyses in this paper. The tutorials give a detailed description of parameters surrounding functions of interest. Dataset examples give a dataset-centric recipe for connecting various analysis components. Download and setup instructions are available. A Docker image has been created and is readily available [56] for ease of access and testing. In addition, more detailed information about both the stable and development version of Giotto is available on https://rubd.github.io/Giotto_site/, which is associated with the Giotto source code from our github repository [57] and under MIT license. These links also provide additional information about installation issues, documentation, extensive information about how to use Giotto, a news section, and guidelines for external contributions. The following datasets were used for demonstrations: seqFISH+ data was obtained from the Cai lab [9], merFISH data was downloaded from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248 [14], osmFISH data was downloaded from https://linnarssonlab.org/osmFISH/ [11], STARmap data was downloaded from https://www.starmapresources.com/data [7], Visium 10X brain and kidney datasets were downloaded from https://www.10xgenomics.com/resources/datasets/, Slide-seq data was obtained from https://portals.broadinstitute.org/single_cell/study/slide-seq-study [8], t-cyCIF data was downloaded from https://www.cycif.org/data/ [10] MIBI data was downloaded from https://www.angelolab.com/mibi-data [13] and CODEX data was downloaded from http://welikesharingdata.blob.core.windows.net/forshare/index.html [12]. Ready to use and preformatted datasets can be found on https://github.com/RubD/spatial-datasets and can be automatically downloaded with the getSpatialDataset function in Giotto. Ruben Dries and Qian Zhu contributed equally to this work. Author Contributions: R.Dr. conceived the project and developed the Giotto analysis software. Q.Z., A.S. and T.Z. developed the Giotto viewer software. G.Y. supervised the project and helped interpret and present the results. R.Do., Q.Z., and R.Dr. developed and implemented spatial enrichment algorithms. H.L. and R.Dr. developed and implemented in silico slicing. Y.F. and R.Dr. implemented 3D visualization. R.Dr., Q.Z., R.Do., H.L., and K.L. analyzed datasets and validated methods. C.H.L.E., F.B., R.E.G., N.P., and L.C. tested the software and provided feedback. R.Dr., Q.Z., and G.Y. wrote the manuscript with input from all authors. All authors read and approved the final manuscript. Ethics approval and consent to participate: Not applicable. Competing interests: The authors declare that they have no competing interests.

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