Appendix C — Related resources

C.1 Introduction

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

C.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).

C.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.

C.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 (Chen et al. 2025; Dries et al. 2021).

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

C.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.

• Harnessing the Potential of Spatial Statistics for Spatial Omics Data with pasta: Online resource describing concepts from spatial statistics that apply to spatial -omics datasets. This online resource also has a preprint published at arXiv:2412.01561.

References

Chen, Jiaji G, Joselyn C Chávez-Fuentes, Matthew O’Brien, et al. 2025. “Giotto Suite: A Multiscale and Technology-Agnostic Spatial Multiomics Analysis Ecosystem.” Nature Methods, 1–13. https://doi.org/10.1038/s41592-025-02817-w.

Dries, Ruben, Qian Zhu, Rui Dong, et al. 2021. “Giotto: A Toolbox for Integrative Analysis and Visualization of Spatial Expression Data.” Genome Biology 22 (78). https://doi.org/10.1186/s13059-021-02286-2.