Software & Workflow for the analysis of imaging mass cytometry data
This repository contains the standard pipelines and scripts used by the cancer immunogenomics group for the analysis of imaging mass cytometry data. Detailed descriptions of the steps to follow are available in the IMC analysis Guide
In short, IMC data is exported from the MCD-viewer following the steps detailed in the guide. Next, data can be normalised using PENGUIN and cell segmentation is performed using cellprofiler and the dedicated cellprofiler pipeline. After data visualisation and cell phenotyping, using ImaCytE and Cytosplore, further analyses can be performed in R-studio.
The repository consists of the following downstream visualisation/analysis scripts:
- 1_File_merging_namematch To combine phenotypes, intensities, cell coordinates and metadata in a large data frame for subsequent analysis
- 2_Variables Contains all variables required for downstream scripts
- 3_Phenotype_Counting To count the abundance of each phenotype per image and sample
- 4_Marker_expression_heatmap To create a heatmap of marker expression per phenotype
- 5_Phenotype_abundance_heatmap To create a heatmap with the abundance of each phenotype per sample
- 6_Phenotype_abundance_graphs To create graphs for each phenotype and their abundance between groups of samples based on provided metadata
- 7_Intensity_counting To count the abundance of each phenotype positive for markers of interest
- 8_Markerpositivity_graphs To create graphs of the counts generated in script 6
- 9a_Phenotype_image_visualisation To visualise the phenotypes onto a selected image
- 9b_Phenotype_image_visualisation_all_ROIs To visualise the phenotypes on all images
- 10_Spatial_neighbour_detection To identify cell-cell neighbourhoods
- 11_Spatial_neighbourhood_plots To plot cell-cell neighbourhood abundances
- 12_Spatial_neighbourhood_hubs To identify multicellular neighbourhoods and plot those
