Chapter 1 Preface

This book provides several examples of computational analysis workflows for spatially-resolved multiomics data, using the Bioconductor framework within the R programming language. The chapters contain details on individual analysis steps as well as complete workflows, with example datasets and R code that can be run on your own laptop.

The book is organized into several parts, including background, preprocessing steps, analysis steps, and complete workflows.

Bioconductor

1.1 Software information and conventions

The R session information when compiling this book is shown below:

sessionInfo()

## R version 4.3.1 (2023-06-16)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.2.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Vienna
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.33     R6_2.5.1          bookdown_0.34     fastmap_1.1.1    
##  [5] xfun_0.39         cachem_1.0.8      knitr_1.43        htmltools_0.5.5  
##  [9] rmarkdown_2.23    cli_3.6.1         sass_0.4.7        jquerylib_0.1.4  
## [13] compiler_4.3.1    rstudioapi_0.15.0 tools_4.3.1       evaluate_0.21    
## [17] bslib_0.5.0       yaml_2.3.7        jsonlite_1.8.7    rlang_1.1.1