phenoimager2mc 0.3.2

Convert PhenoImager output .tif tiles to a single OME-TIFF with correct metadata

phenoimager2mc

Formatting PhenoImager .tif output files to be compatible with the MCMCIRO pipeline and ASHLAR.

Description

Raw data: The PhenoImager software outputs one float32 .tif file per tile and cycle containing all channels. The metadata is unstandardized. Goal: For analysing the data within MCMCIRO (initial registration with ASHLAR), one stacked ome-tif file per channel containing all tiles and cycles is required. Steps in this module:

• Extraction of metadata from unstandardized tif files
• Creation of stacked and correct ome-tiff files readable for ASHLAR
• Conversion from float32 to uint16
• Normalization to max or 99th percentile (user's choice)

Usage

CLI

Input

The CLI script scripts/phenoimager2mc.py requires 3 inputs

• The path to the folder containing all .tif files from one cycle with -i or --input
• The number of markers that was used in this cycle with -m or --num_markers
• The normalization method that the intensities per cycle should be normalized with. Either 99th or max with -n or --normalization

Output

• Output .tif file containing all tiles and channels of one cycle with -o or --output

Docker usage

If you want to run the module directly from a pre-configured container with all the required packages, you can either build the docker container yourself or pull it from the Github container registry.

To build the container run:

git clone https://github.com/SchapiroLabor/phenoimager2mc.git
docker build -t phenoimager2mc:latest .
docker run phenoimager2mc:latest python phenoimager2mc.py

To pull the container from the Github container registry (ghcr.io):

## Login to ghcr.io
docker login ghcr.io

## Pull container
docker pull ghcr.io/schapirolabor/phenoimager2mc:latest

Installation

Option 1: Install from PyPI

pip install phenoimager2mc
phenoimager2mc --help

Option 2: Docker

Pull the image:

docker pull ghcr.io/schapirolabor/phenoimager2mc:latest

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

docker run --rm -v $(pwd):/data ghcr.io/schapirolabor/phenoimager2mc:latest \
    phenoimager2mc \
    -i /data/input_dir \
    -o /data/output.ome.tif \
    -n 6 \
    -m 99th

Option 3: Development/Conda environment

For development or reproducible research setups:

git clone https://github.com/SchapiroLabor/phenoimager2mc.git
cd Background_subtraction
conda env create -f environment.yml
conda activate phenoimager2mc_env

pip install -e .

run

phenoimager2mc --help