Appendix B — Related resources

B.1 Introduction

This chapter provides links to several related resources from the Bioconductor and other communities.

B.2 Data preprocessing procedures

• Visium data preprocessing: Online book containing details on data preprocessing procedures for spatial transcriptomics data from the 10x Genomics Visium platform (using tools outside R and Bioconductor).

B.3 Resources for other spatial omics platforms

Workflows and other resources for data from other spatial omics platforms:

• Analysis workflow for IMC data: Online book providing a workflow highlighting the use of R/Bioconductor packages to analyze single-cell data obtained from segmented imaging mass cytometry (IMC) images. Examples focus on IMC data and can also be applied to images obtained by other highly-multiplexed imaging technologies, e.g. CODEX, MIBI, and mIF.

• VectraPolarisData: Bioconductor data package providing multiplex single-cell imaging datasets collected on Vectra Polaris and Vectra 3 instruments.

B.4 Data structures

Data structures for storing data from spatial transcriptomics and other spatial omics platforms outside R/Bioconductor:

• AnnData: Python class for storing single-cell and spatial data in the scverse framework.

• Giotto classes: R classes used to store spatial omics data within the Giotto Suite framework Dries et al. (2021).

• SpatialData: Python class for storing data from spatial transcriptomics and other spatial omics platforms.

B.5 Statistical concepts

• Modern Statistics for Modern Biology: Online textbook on concepts in modern statistics for high-throughput and high-dimensional biology. This book includes a detailed chapter on image data and spatial statistics.

References

Dries, Ruben, Qian Zhu, Rui Dong, Chee-Huat Linus Eng, Huipeng Li, Kan Liu, Yuntian Fu, et al. 2021. “Giotto: A Toolbox for Integrative Analysis and Visualization of Spatial Expression Data.” Genome Biology 22. https://doi.org/10.1186/s13059-021-02286-2.