Imaging mass cytometry (IMC) profiling of upper tract urothelial cancer

Organization

• The metadata directory contains metadata relevant to annotate the samples
• This CSV file is the master record of all analyzed samples
• The src directory contains source code used to analyze the data
• Raw data (i.e. MCD files) will be under the data directory.
• Processing of the data will create TIFF files under the processed directory.
• Outputs from the analysis will be present in a results directory, with subfolders pertaining to each part of the analysis as described below.

Raw data in the form of MCD files are hosted on WCM's enterprise version of Box.com. An account is needed to download the files, which can be made programmatically with the imctransfer program. For now you'll need a developer token to connect to box.com programmatically. Place the credentials in a JSON file in ~/.imctransfer.auth.json. Be sure to make the file read-only (e.g. chmod 400 ~/.imctransfer.auth.json).

Pre-processing of the MCD files into images is done with imcpipeline.

Reproducibility

Running

To see all available steps type:

$ make help

Makefile for the utuc-imc project/package.
Available commands:
help            Display help and quit
requirements    Install Python requirements
transfer        Transfer data from wcm.box.com to local environment
prepare         Run first step of convertion of MCD to various files
process_local   Run IMC pipeline locally
process_scu     Run IMC pipeline on SCU
checkfailure    Check whether any samples failed during preprocessing
fail            Check whether any samples failed during preprocessing
checksuccess    Check which samples succeded during preprocessing
succ            Check which samples succeded during preprocessing
rename_outputs  Rename outputs from CellProfiler output to values expected by `imc`
rename_outputs_back     Rename outputs from values expected by `imc` to CellProfiler
sync            Sync code to SCU server

To reproduce analysis, simply do:

$ make requirements
$ make

Requirements

• Python 3.7+ (running on 3.8.2)
• Python packages as specified in the requirements file - install with make requirements or pip install -r requirements.txt.

Virtual environment

It is recommended to compartimentalize the analysis software from the system's using virtual environments, for example.

Here's how to create one with the repository and installed requirements:

git clone [email protected]:ElementoLab/utuc-imc.git
cd utuc-imc
virtualenv .venv
source activate .venv/bin/activate
pip install -r requirements.txt