12  Spatial co-localization

12.1 Introduction

In this chapter, we will show examples of analyses to investigate spatial co-localization of cell types.

For these analyses, we will use a different dataset that provides single-cell spatial resolution, e.g. from the 10x Genomics Xenium platform.

12.2 Load previously saved data

We start by loading the previously saved data object(s) (see Section 10.4).

library(SpatialExperiment)
spe <- readRDS("spe_clustering.rds")

12.3 Spatial co-localization of cell types

Methods available from Bioconductor for this type of analysis include:

• hoodscanR (Liu et al. 2024)

• spicyR (Canete et al. 2022)

References

Canete, Nicolas P., Sourish S. Iyengar, John T. Ormerod, Heeva Baharlou, Andrew N. Harman, and Ellis Patrick. 2022. “spicyR: Spatial Analysis of in Situ Cytometry Data in R.” Bioinformatics 38: 3099–3105. https://doi.org/10.1093/bioinformatics/btac268.

Liu, Ning, Jarryd Martin, Dharmesh D. Bhuva, Jinjin Chen, Mengbo Li, Samuel C. Lee, Malvika Kharbanda, et al. 2024. “hoodscanR: Profiling Single-Cell Neighborhoods in Spatial Transcriptomics Data.” bioRxiv. https://doi.org/10.1101/2024.03.26.586902.