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IMC data analysis tools
This repository contains an overview of available tools to use for imaging mass cytometry™ (IMC™) data analysis.
Image processing
Common tasks for IMC data analysis first include image pre-processing, segmentation, feature extraction and data export. The initial IMCSegmentationPipeline was developed to allow manual image processing for IMC and other multiplexed imaging data.
Building up on the IMCSegmentationPipeline, the steinbock framework offers user-friendly processing of multi-channel images and supports different segmentation strategies.
IMC Segmentation Pipeline
The ImcSegmentationPipeline uses the imctools python package to handle IMC data. Custom CellProfiler plugins support multi-channel image analysis within CellProfiler. In addition, Ilastik is needed to perform pixel classification.
ImcSegmentationPipeline
ImcSegmentationPipeline Docs
ImcPluginsCP
ImcPluginsCP Docs
CellProfiler
CellProfiler Docs
steinbock
The steinbock framework uses the readimc python package for IMC-specific pre-processing. Image processing can be performed via a command line interface.
Viewers for IMC data
The raw IMC data files in Fluidigm® MCD™ format can be read in and visualized using the MCD viewer (only supported on Windows).
A napari plugin is now available to read in raw IMC data files and visualize them in a shared coordinate system:
After processing using the ImcSegmentationPipeline or steinbock, raw MCD files are converted into single-channel TIFF files which can be read in and visualized using histoCAT. histoCAT also allows single-cell and spatial data analysis.
histoCAT
histoCAT Docs
histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data
A web-application of histoCAT is available at:
histoCAT-web
histoCAT-web Docs
Downstream analysis
After pre-processing and feature extraction, single-cell expression data can be read into R and analysed using standardized approaches. An overview on how to analyse single-cell data generated by multiplexed imaging can be seen here:
Reading in data
The imcRtools package allows reading in spatially annotated single-cell data extracted from IMC raw data using the ImcSegmentationPipeline or steinbock. In R, single-cell data are stored in either a SpatialExperiment or SingleCellExperiment object.
SpatialExperiment
SpatialExperiment Docs
SingleCellExperiment
SingleCellExperiment Docs
Single-cell analysis
Within the Bioconductor project, a number of single-cell analysis tools have been developed. The Orchestrating Single-Cell Analysis with Bioconductor book offers an overview on common analysis strategies. Here, we name a number of individual packages for different tasks.
scran for graph-based clustering
scater for dimensionality reduction and data visualization
dittoSeq for data visualziation
CATALYST for clustering, data visualization and differential analysis
Spatial analysis
The imcRtools package offers a number of functions to perform spatial analysis of cells extracted from multi-channel images.
Image visualization
In R, the cytomapper package allows pixel- and cell-level visualization of multiplexed imaging data.
cytomapper
cytomapper Docs
cytomapper: an R/Bioconductor package for visualization of highly multiplexed imaging data
Example datasets
To access example IMC datasets, we have build the imcdatasets Bioconductor package.