imcRtools

This R/Bioconductor package contains helper functions to analyse IMC (or other multiplexed imaging) data.

Its official package page can be found here: https://bioconductor.org/packages/imcRtools

Bug notice: we discovered and fixed a bug in the testInteractions function in version below 1.5.5 which affected SingleCellExperiment or SpatialExperiment objects in which cells were not grouped by image. Please install the newest version of imcRtools directly from Github as explained below.

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Release
Devel

Introduction

Highly multiplexed imaging techniques such as imaging mass cytometry (IMC), multiplexed ion beam imaging (MIBI) and cyclic immunofluorescence techniques acquire read-outs of the expression of tens of protein in a spatially resolved manner.

This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected.

Installation

The imcRtools package can be installed from Bioconductor via:

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

BiocManager::install("imcRtools")

The development version of imcRtools can be installed from Github via:

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

remotes::install_github("BodenmillerGroup/imcRtools")

Getting help

The analysis of highly multiplexed imaging data requires multiple pre-processing and diverse analysis steps.

1. Processing of raw data and segmentation: The ImcSegmentationPipeline and the steinbock library can be used to process and segment IMC data. The imcRtools package provides reader functions for outputs generated by these approaches.

2. Single-cell analysis using the Bioconductor framework: The Orchestrating Single-Cell Analysis with Bioconductor book is an excellent resource for beginners and advanced analysis concerning single-cell data. Common analysis steps include dimensionality reduction, unsupervised clustering for cell type detection and data visualization. The imcRtools package supports data structures that fully integrate with the analysis presented in the OSCA book.

3. Handling multiplexed images in R: the cytomapper Bioconductor package provides functions and data structure to handle and analyse highly multiplexed imaging data (images, masks and single-cell data) natively in R.

Citation

Please cite the following paper when using imcRtools in your research:

Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). https://doi.org/10.1038/s41596-023-00881-0

@article{Windhager2023,
    author = {Windhager, Jonas and Zanotelli, Vito R.T. and Schulz, Daniel and Meyer, Lasse and Daniel, Michelle and Bodenmiller, Bernd and Eling, Nils},
    title = {An end-to-end workflow for multiplexed image processing and analysis},
    year = {2023},
    doi = {10.1038/s41596-023-00881-0},
    URL = {https://www.nature.com/articles/s41596-023-00881-0},
    journal = {Nature Protocols}
}

Contributing

For feature requests, please open an issue here.

Alternatively, you can fork the repository, add your change and issue a pull request.

Maintainer

Daniel Schulz