cytomapper
R package for visualization of highly multiplexed imaging data
Science Score: 59.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: nature.com -
✓Committers with academic emails
2 of 8 committers (25.0%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (14.9%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
R package for visualization of highly multiplexed imaging data
Basic Info
- Host: GitHub
- Owner: BodenmillerGroup
- Language: R
- Default Branch: devel
- Homepage: https://bodenmillergroup.github.io/cytomapper/
- Size: 221 MB
Statistics
- Stars: 33
- Watchers: 7
- Forks: 9
- Open Issues: 10
- Releases: 2
Topics
Metadata Files
README.md
cytomapper
R/Bioconductor package to spatially visualize pixel- and cell-level information obtained from highly multiplexed imaging.
Its official package page can be found here: https://bioconductor.org/packages/cytomapper
Check status
| Bioc branch | Checks |
|:-----------:|:------:|
| Release |
|
| Devel |
|
Introduction
Highly multiplexed imaging acquires single-cell expression values of selected proteins in a spatially-resolved fashion.
These measurements can be visualized across multiple length-scales.
First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution.
Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualized on segmented cell areas.
This package contains functions for the visualization of multiplexed read-outs and cell-level information obtained by multiplexed imaging cytometry.
The main functions of this package allow 1. the visualization of pixel-level information across multiple channels (plotPixels), 2. the display of cell-level information (expression and/or metadata) on segmentation masks (plotCells) and 3. gating + visualization of cells on images (cytomapperShiny).
The cytomapper package provides toy data that were generated using imaging mass cytometry [1] taken from Damond et al. [2].
For further instructions to process raw imaging mass cytometry data, please refer to the IMC Segmentation Pipeline and the histoCAT as alternative visualization tool.
Requirements
The cytomapper package requires R version >= 4.0.
It builds on data objects and functions contained in the SingleCellExperiment and EBImage packages.
Therefore, these packages need to be installed (see below).
Installation
The cytomapper package can be installed from Bioconductor via:
```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("cytomapper") ```
The development version of the cytomapper package can be installed from Github using remotes in R.
Please make sure to also install its dependecies:
```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c("EBImage", "SingleCellExperiment"))
install.packages("remotes")
remotes::installgithub("BodenmillerGroup/cytomapper", buildvignettes = TRUE, dependencies = TRUE) ```
To load the package in your R session, type the following:
r
library(cytomapper)
Functionality
The cytomapper package offers three main functions: plotPixels, plotCells and cytomapperShiny.
plotPixels
The function takes a CytoImageList object (available via the cytomapper package) containing multi-channel images representing pixel-level expression values and optionally a CytoImageList object containing segementation masks and a SingleCellExperiment object containing cell-level metadata.
It allows the visualization of pixel-level information of up to six channels and outlining cells based on cell-level metadata. To see the full functionality in R type:
r
?plotPixels
plotCells
This function takes a CytoImageList object containing segementation masks and a SingleCellExperiment object containing cell-level mean expression values and metadata information.
It allows the visualization of cell-level expression data and metadata information. To see the full functionality in R type:
r
?plotCells
cytomapperShiny
This Shiny application allows gating of cells based on their expression values and visualises selected cells on their corresponding images.
It requires at least a SingleCellExperiment as input and optionally CytoImageList objects containing segmentation masks and multi-channel images.
For full details, please refer to:
r
?cytomapperShiny
Getting help
For more information on processing imaging mass cytometry data, please refer to the IMC Segmentation Pipeline.
This pipeline generates multi-channel tiff stacks containing the pixel-level expression values and segementation masks that can be used for the plotting functions in the cytomapper package.
More information on how to work with and generate a SingleCellExperiment object can be obtained from: Orchestrating Single-Cell Analysis with Bioconductor
An extensive introduction to image analysis in R can be found at: Introduction to EBImage
A full overview on the analysis workflow and functionality of the cytomapper package can be found by typing:
r
vignette("cytomapper")
For common issues regarding the cytomapper package, please refer to the wiki.
Demonstrations
To see example usage of the cytomapper package, please refer to its publication repository and a number of workshop demonstrations.
Citation
Please cite cytomapper as:
Nils Eling, Nicolas Damond, Tobias Hoch, Bernd Bodenmiller (2020). cytomapper: an R/Bioconductor package for visualization of highly
multiplexed imaging data. Bioinformatics, doi: 10.1093/bioinformatics/btaa1061
Authors
Nils Eling nils.eling 'at' dqbm.uzh.ch
Maintainer
References
Owner
- Name: BodenmillerGroup
- Login: BodenmillerGroup
- Kind: organization
- Repositories: 83
- Profile: https://github.com/BodenmillerGroup
GitHub Events
Total
- Issues event: 5
- Watch event: 2
- Issue comment event: 3
- Push event: 5
- Fork event: 1
Last Year
- Issues event: 5
- Watch event: 2
- Issue comment event: 3
- Push event: 5
- Fork event: 1
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| nilseling | n****g@g****e | 633 |
| Tobias Hoch | t****h@b****h | 54 |
| ndamond | n****d@g****m | 19 |
| lassedochreden | l****r@u****h | 11 |
| Nitesh Turaga | n****a@g****m | 10 |
| J Wokaty | j****y@s****u | 10 |
| A Wokaty | a****y@s****u | 2 |
| Carson | c****1@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 71
- Total pull requests: 25
- Average time to close issues: 6 months
- Average time to close pull requests: about 10 hours
- Total issue authors: 22
- Total pull request authors: 4
- Average comments per issue: 1.72
- Average comments per pull request: 0.72
- Merged pull requests: 22
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 0
- Average time to close issues: 4 months
- Average time to close pull requests: N/A
- Issue authors: 2
- Pull request authors: 0
- Average comments per issue: 1.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- nilseling (45)
- toobiwankenobi (4)
- DarioS (2)
- kaizen89 (2)
- ndamond (1)
- tessbrodie (1)
- JanaFischer (1)
- SchulzDan (1)
- jparkinson375 (1)
- ellispatrick (1)
- MureziCapaul (1)
- Elena983 (1)
- ssaintsoon (1)
- Pancreas-Pratik (1)
- ynanli (1)
Pull Request Authors
- nilseling (18)
- toobiwankenobi (4)
- lassedochreden (2)
- cpsievert (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- bioconductor 24,971 total
- Total dependent packages: 3
- Total dependent repositories: 0
- Total versions: 5
- Total maintainers: 1
bioconductor.org: cytomapper
Visualization of highly multiplexed imaging data in R
- Homepage: https://github.com/BodenmillerGroup/cytomapper
- Documentation: https://bioconductor.org/packages/release/bioc/vignettes/cytomapper/inst/doc/cytomapper.pdf
- License: GPL (>= 2)
-
Latest release: 1.20.0
published 9 months ago
Rankings
Maintainers (1)
Dependencies
- EBImage * depends
- R >= 4.0 depends
- SingleCellExperiment * depends
- methods * depends
- BiocParallel * imports
- DelayedArray * imports
- HDF5Array * imports
- RColorBrewer * imports
- S4Vectors * imports
- SpatialExperiment * imports
- SummarizedExperiment * imports
- ggbeeswarm * imports
- ggplot2 * imports
- grDevices * imports
- graphics * imports
- matrixStats * imports
- nnls * imports
- raster * imports
- rhdf5 * imports
- shiny * imports
- shinydashboard * imports
- stats * imports
- svgPanZoom * imports
- svglite * imports
- tools * imports
- utils * imports
- viridis * imports
- BiocStyle * suggests
- cowplot * suggests
- knitr * suggests
- markdown * suggests
- rmarkdown * suggests
- shinytest * suggests
- testthat * suggests
- actions/cache v2 composite
- actions/checkout v2 composite
- actions/cache v2 composite
- actions/checkout v2 composite
- actions/cache v1 composite
- actions/checkout v2 composite
- r-lib/actions/setup-pandoc v2 composite
- r-lib/actions/setup-r v2 composite