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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](https://bioconductor.org/packages/imcRtools)
## Check status
| Bioc branch | Checks |
| Release |[![build-check-release](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/build-checks-release.yml/badge.svg?branch=master)](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/build-checks-release.yml)|
| Devel |[![build-check-devel](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/build-checks-devel.yml/badge.svg?branch=master)](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/build-checks-devel.yml)|
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
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.
The `imcRtools` package can be installed from `Bioconductor` via:
if (!requireNamespace("BiocManager", quietly = TRUE))
The development version of `imcRtools` can be installed from Github via:
if (!requireNamespace("remotes", quietly = TRUE))
## 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
library can be used to process and segment IMC data. The
`imcRtools` package provides reader functions for outputs generated by these
2. Single-cell analysis using the [Bioconductor](https://www.bioconductor.org/) framework: The
[Orchestrating Single-Cell Analysis with Bioconductor](https://bioconductor.org/books/release/OSCA/)
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
Bioconductor package provides functions and data structure to handle and
analyse highly multiplexed imaging data (images, masks and single-cell data)
natively in R.
Please cite `imcRtools` as:
Jonas Windhager, Bernd Bodenmiller, Nils Eling (2020). An end-to-end workflow for multiplexed image processing and analysis.
bioRxiv, doi: 10.1101/2021.11.12.468357
For feature requests, please open an issue [here](https://github.com/BodenmillerGroup/imcRtools/issues).
Alternatively, you can fork the repository, add your change and issue a pull request.