SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor

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Abstract

Summary

SpatialExperiment is a new data infrastructure for storing and accessing spatially-resolved transcriptomics data, implemented within the R/Bioconductor framework, which provides advantages of modularity, interoperability, standardized operations and comprehensive documentation. Here, we demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms, and provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData and ggspavis packages.

Availability and implementation

The SpatialExperiment, STexampleData, TENxVisiumData and ggspavis packages are available from Bioconductor. The package versions described in this manuscript are available in Bioconductor version 3.15 onwards.

Supplementary information

Supplementary data are available at Bioinformatics online.

Related collections

Most cited references 28

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Is Open Access

Integrated analysis of multimodal single-cell data

Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen … (2021)

Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

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Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.

Patrik L. Ståhl, Fredrik Salmén, Sanja Vickovic … (2016)

Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

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Simple Features for R: Standardized Support for Spatial Vector Data

Edzer Pebesma (2018)

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All references

Author and article information

Contributors

Dario Righelli:

ORCID: https://orcid.org/0000-0003-1504-3583

Lukas M Weber

Helena L Crowell:

ORCID: https://orcid.org/0000-0002-4801-1767

Brenda Pardo:

ORCID: https://orcid.org/0000-0001-8103-7136

Leonardo Collado-Torres:

ORCID: https://orcid.org/0000-0003-2140-308X

Shila Ghazanfar:

ORCID: https://orcid.org/0000-0001-7861-6997

Aaron T L Lun:

ORCID: https://orcid.org/0000-0002-3564-4813

Stephanie C Hicks:

ORCID: https://orcid.org/0000-0002-7858-0231

Davide Risso:

ORCID: https://orcid.org/0000-0001-8508-5012

Valentina Boeva: Role: Associate Editor

Journal

Journal ID (nlm-ta): Bioinformatics

Journal ID (iso-abbrev): Bioinformatics

Journal ID (publisher-id): bioinformatics

Title: Bioinformatics

Publisher: Oxford University Press

ISSN (Print): 1367-4803

ISSN (Electronic): 1367-4811

Publication date Collection: 01 June 2022

Publication date (Electronic): 28 April 2022

Publication date PMC-release: 28 April 2022

Volume: 38

Issue: 11

Pages: 3128-3131

Affiliations

Department of Statistical Sciences, University of Padova, 35121 Padova, Italy

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA

Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

SIB Swiss Institute of Bioinformatics , Zurich, Switzerland

Escuela Nacional de Estudios Superiores Unidad Juriquilla, Universidad Nacional Autónoma de México, Queretaro 76230, Mexico

Lieber Institute for Brain Development, Baltimore, MD 21205, USA

Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom

Genentech, South San Francisco, CA 94080, USA

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA

Department of Statistical Sciences, University of Padova, 35121 Padova, Italy

Author notes

[†]

The authors wish it to be known that, in their opinion, the first three authors and last two authors should be regarded as Joint Authors.

To whom correspondence should be addressed. davide.risso@ 123456unipd.it or shicks19@ 123456jhu.edu

Author information

Dario Righelli https://orcid.org/0000-0003-1504-3583

Helena L Crowell https://orcid.org/0000-0002-4801-1767

Brenda Pardo https://orcid.org/0000-0001-8103-7136

Leonardo Collado-Torres https://orcid.org/0000-0003-2140-308X

Shila Ghazanfar https://orcid.org/0000-0001-7861-6997

Aaron T L Lun https://orcid.org/0000-0002-3564-4813

Stephanie C Hicks https://orcid.org/0000-0002-7858-0231

Davide Risso https://orcid.org/0000-0001-8508-5012

Article

Publisher ID: btac299

DOI: 10.1093/bioinformatics/btac299

PMC ID: 9154247

PubMed ID: 35482478

SO-VID: ec6dced9-7487-485c-890e-20803495b112

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 18 August 2021

Date revision received : 02 February 2022

Date: 19 April 2022

Date accepted : 25 April 2022

Date: 12 May 2022

Page count

Pages: 4

Funding

Funded by: Chan Zuckerberg Initiative DAF;

Award ID: CZF2019-002443

Funded by: Silicon Valley Community Foundation, DOI 10.13039/100000923;

Funded by: National Institutes of Health/NIMH;

Award ID: U01MH122849

Funded by: Programma per Giovani Ricercatori Rita Levi Montalcini’ granted by the Italian Ministry of Education;

Funded by: University, and Research and by the National Cancer Institute of the National Institutes of Health;

SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor

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