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.