We provide an example image-based profiling pipeline using pycytominer
and cytominer-eval
.
We use publicly-available Cell Painting datasets to demonstrate how to use tools in the cytominer ecosystem.
- Step 0.0 - Install miniconda.
- After installing miniconda, restart your terminal. You should now see the (base) prefix on your command line.
- Optionally, also install mamba with
conda install mamba -c conda-forge
- Step 0.1 - Install aws cli
- Step 0.2 - Clone this github repository (forking is optional):
# Make sure you are navigated to the directory of your choice
git clone [email protected]:cytomining/pipeline-examples.git
- Step 0.3 - Create the conda environment, which includes pycytominer and cytominer-eval packages.
# Make sure you navigated into the repository folder after cloning
# cd pipeline-examples
conda env create --force --file environment.yml
# Or, if you installed mamba
mamba env create --force --file environment.yml
- Step 0.4 - Activate the conda environment
conda activate pycytominer-example
- [Optional] Step 0.5 - Alternatively, the two packages (pycytominer and cytominer-eval) can be installed via
pip
pip install git+https://github.com/cytomining/pycytominer@8e3c28d3b81efd2c241d4c792edfefaa46698115
pip install git+https://github.com/cytomining/cytominer-eval@6f9d350badd0a18b6c1a76171813aaf9a52f8d9f
Data are not included in this repository.
You must run the code in 0.download.sh
, which requires the AWS command line interface.
# Download one example plate from AWS
./0.download.sh
- Step 2.0 - Run the command
jupyter lab
in your terminal, in the top level directory.
# Make sure the pycytominer-example environment is activated
jupyter lab
- Step 2.1 - Navigate to
1.profile.ipynb
and follow along!
- Step 3.0 - Navigate to
2.evaluate.ipynb
and follow along!