A.1 Overview
In this chapter, we highlight several related resources from the Bioconductor and other communities.
A.2 Data preprocessing procedures for the Visium platform
Data preprocessing procedures for spatial transcriptomics data from the 10x Genomics Visium platform:
- 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).
A.3 Resources for other spatial omics platforms
Workflows and other resources for other spatial omics platforms:
Analysis workflow for IMC data: Online book providing a workflow highlighting the use of common 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 two multiplex single-cell imaging datasets collected on Vectra Polaris and Vectra 3 instruments.
A.4 Data structures
Data structures for storing spatial transcriptomics and other spatial omics data that have not already been discussed in Chapter 3:
AnnData: Python class for storing single-cell and spatial data within the scverse framework.
SpatialData: Python class for storing data from spatial transcriptomics and other spatial omics platforms.
A.5 Statistical concepts
- Modern Statistics for Modern Biology: Online textbook on concepts in modern statistics for high-throughput and high-dimensional biology, including chapter on image data and spatial statistics.