imc2mc 0.0.3

Convert Imaging Mass Cytometry (IMC) output .txt files to OME-TIFF

imc2mc

Formatting Imaging Mass Cytrometry (IMC) output files to be compatible with the MCMICRO pipeline.

Description

The Hyperion imaging system outputs one .mcd file per slide containing multiple acquisitions as well as one .txt file per acquisition. This script currently uses the .txt files to create a float32 .tif file with corresponding OME-XML metadata per acquisition. To transform the .txt. file to a .tif file we use the readimc package by BodenmillerGroup. Hot pixel filtering is based on the Steinbock pipeline.

Steps in this module:

• create .tif file from .txt file
• add OME-XML metadata to .tif file

Usage

CLI

Input

The CLI script imc2mc requires 3 inputs

• The path to the acquisition .txt file with -i or --input
• The pixel size in um with -p or --pixel_size
• The output .tif file with -o or --output. Folder structure will be created if not present.

Optional input:

• To apply hot pixel filtering, input an integer with -t or --hp_threshold. Based on Steinbock we recommend a threshold of 50.
• The current version can be accessed with -vor --version

Installation

Option 1: Install from PyPI

pip install imc2mc
imc2mc --help

Option 2: Docker

Pull the image:

docker pull ghcr.io/schapirolabor/imc2mc:latest

and run the tool directly, mounting your input and output directories:

docker run --rm -v $(pwd):/data ghcr.io/schapirolabor/imc2mc:latest \
    imc2mc \
    -i /data/input_dir \
    -o /data/output.ome.tif \
    -p 1 \
    -t 50

Option 3: Development/Conda environment

For development or reproducible research setups:

git clone https://github.com/SchapiroLabor/imc2mc.git
cd Background_subtraction
conda env create -f environment.yml
conda activate imc2mc_env

pip install -e .

run

imc2mc --help