Cytomapper paper

The scripts contained in this repository serve the purpose of testing, validating and publishing the cytomapper package.

Setting up the environment

For reproducibility purposes, we use Docker and workflowr to organize the scripts and the computational environment. Please follow these steps to set-up and run the analysis presented in this repository:

1. Install Docker
2. Pull the docker image

docker pull nilseling/bioconductor_cytomapper:0.0.3

3. Run the docker image

docker run -e PASSWORD=bioc -p 8787:8787 nilseling/bioconductor_cytomapper:0.0.3

Here, the set PASSWORD is bioc. This will be used to login to RStudio later.

4. Open a browser window at http://localhost:8787/
5. Sign in to RStudio using Username: rstudio and Password: bioc

You have now a running instance of all the software needed to reproduce the analysis.

Running the code

The following steps will guide you through running the analsysis:

1. Within RStudio, navigate to cytmapper_publication
2. By clicking cytomapper_publication.Rproj, open the correct R project
3. Navigate to analysis and run the scripts in the provided order

Further instructions can be found in the individual scripts.

Installing cytomapper

The cytomapper version for the Bioinformatics publication can be installed via:

install.packages(c("devtools", "workflowr", "tidyverse"))
devtools::install_github("BodenmillerGroup/[email protected]")

The cytomapper version used for the bioRxiv submission can be installed via:

install.packages(c("devtools", "workflowr", "tidyverse"))
devtools::install_github("BodenmillerGroup/[email protected]")

The Bioconductor release version of cytomapper can be obtained from Bioconductor. The following code will also install additional packages needed to perform the analysis.

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

BiocManager::install(c("cytomapper", "workflowr", "tidyverse"))

The Bioconductor development version of cytomapper can be installed via:

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

BiocManager::install(version = "devel", update = TRUE, ask = FALSE)

BiocManager::install(c("cytomapper", "workflowr", "tidyverse"))

Data

The example dataset has been published in: https://www.cell.com/cell-metabolism/fulltext/S1550-4131(18)30691-0

The dataset is available for download from Mendeley Data: http://dx.doi.org/10.17632/cydmwsfztj.2

Specifically, the following files are used in the current analysis:

• CellSubset: Single cell data for a subset of 100 images from the original publication.
• ImageSubset: Image stacks for a subset of 100 images from the original publication.
• Masks: Cell masks as TIFF files.
• Image: Image metadata.
• CellTypes: Cell type information.
• Donors: Pancreas donors metadata.
• Panel: Antibody panel.
• ChannelMass: File used to match channels (stack slices) and metals (antibodies).

For more information, please refer to the data folder.

Furthermore, the presented data has been deposited on ExperimentHub an can be accessed using the imcdatasets package. More details can be found in the 03-DataAccess script.

Citation

Please cite cytomapper as:

Nils Eling, Nicolas Damond, Tobias Hoch, Bernd Bodenmiller (2020). cytomapper: an R/Bioconductor package for visualisation of highly
  multiplexed imaging data. Bioinformatics, https://doi.org/10.1093/bioinformatics/btaa1061