@nilseling
@NilsEling

1 Data and code availability

To follow this tutorial, please visit https://github.com/BodenmillerGroup/demos/tree/main/docs. The compiled .html file of this workshop is hosted at: https://bodenmillergroup.github.io/demos.

We will need to install the following packages for the workshop:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("imcRtools", "tidyverse", "patchwork",
                       "ggplot2", "viridis", "pheatmap", "scales")))

To reproduce the analysis, clone the repository:

git clone https://github.com/BodenmillerGroup/demos.git

and open the EuroBioc2022_workshop.Rmd file in the docs folder.

2 Introduction

Highly multiplexed imaging enables the simultaneous detection of tens of biological molecules (e.g. proteins, RNA; also referred to as “markers”) in their spatial tissue context. Recently established multiplexed imaging technologies rely on cyclic staining with immunofluorescently-tagged antibodies (Lin et al. 2018; Gut, Herrmann, and Pelkmans 2018), or the use of oligonucleotide-tagged (Saka et al. 2019; Goltsev et al. 2018) or metal-tagged antibodies (Giesen et al. 2014; Angelo et al. 2014), among others. Across technologies, the acquired data are commonly stored as multi-channel images, where each pixel encodes the abundance of all acquired markers at a specific position in the tissue. After data acquisition, bioimage processing and segmentation are conducted to extract data for downstream analysis. When performing end-to-end multiplexed image analysis, the user is often faced with a diverse set of computational tools and complex analysis scripts. We developed an interoperabale, modularized computational workflow to process and analyze multiplexed imaging data (Figure 1). The steinbock framework facilitates multi-channel image processing including raw data pre-processing, image segmentation and feature extraction. Data generated by steinbock can be directly read by the imcRtools R/Bioconductor package for data visualization and spatial analysis (Figure 1). The cytomapper package support image handling and composite as well as segmentation mask visualization.