Install immuneML with a package manager
This manual shows how to install immuneML using either conda or pip.
Install immuneML with conda
If a conda distribution is not already installed on the machine, see the official conda installation documentation.
Once conda is working, create a directory for immuneML and navigate to the directory:
mkdir immuneML/ cd immuneML/
Create a virtual environment using conda. immuneML has been tested extensively with Python versions 3.7 and 3.8, but not 3.9. To create a conda virtual environment with Python version 3.8, use:
conda create --prefix immuneml_env/ python=3.8
Activate the created environment:
conda activate immuneml_env/
To install immuneML using conda, run:
conda install -c bioconda immuneml
Install immuneML with pip
0. To install immuneML with pip, make sure to have Python version 3.7 or 3.8 installed. immuneML with later Python versions should also work, but it has not been extensively tested. For more information on Python versions, see the official Python website.
1. Create a virtual environment where immuneML will be installed. It is possible to install immuneML as a global package, but it is not recommended as there might be conflicting versions of different packages. For more details, see the official documentation on creating virtual environments with Python. To create an environment, run the following in the terminal (for Windows-specific commands, see the virtual environment documentation linked above):
python3 -m venv ./immuneml_venv/
To activate the virtual environment on Mac/Linux, run the following command (for Windows, see the documentation in the previous step):
If not already up-to-date, update pip:
python3 -m pip install --upgrade pip
If not already installed, install the wheel package. If it is not installed, the installation of some of the dependencies might default to legacy ‘setup.py install’.
pip install wheel
To install immuneML from PyPI in this virtual environment, run the following:
pip install immuneML
Alternatively, if you want to use the TCRdistClassifier ML method and corresponding TCRdistMotifDiscovery report, include the optional extra
pip install immuneML[TCRdist]
See also this question under ‘Troubleshooting’: I get an error when installing PyTorch (could not find a version that satisfies the requirement torch)
Installing optional dependencies
Optionally, if you want to use the DeepRC ML method and and corresponding DeepRCMotifDiscovery report, you also
have to install DeepRC dependencies using the
Important note: DeepRC uses PyTorch functionalities that depend on GPU. Therefore, DeepRC does not work on a CPU.
To install the DeepRC dependencies, run:
pip install -r requirements_DeepRC.txt --no-dependencies
If you want to use the CompAIRRDistance or CompAIRRSequenceAbundance encoder, you have to install the C++ tool CompAIRR.
The easiest way to do this is by cloning CompAIRR from GitHub and installing it using
make in the main folder:
git clone https://github.com/uio-bmi/compairr.git cd compairr make install
If such installation is unsuccessful (for example if you do not have the rights to install CompAIRR via make), it is also possible to directly provide the path to a CompAIRR executable as a parameter to CompAIRRDistance or CompAIRRSequenceAbundance encoder.
To validate the installation, run:
The output should look like this:
usage: immune-ml [-h] [--tool TOOL] specification_path result_path immuneML command line tool positional arguments: specification_path Path to specification YAML file. Always used to define the analysis. result_path Output directory path. optional arguments: -h, --help show this help message and exit --tool TOOL Name of the tool which calls immuneML. This name will be used to invoke appropriate API call, which will then do additional work in tool-dependent way before running standard immuneML. --version show program's version and exit
To quickly test out whether immuneML is able to run, try running the quickstart command:
This will generate a synthetic dataset and run a simple machine machine learning analysis on the generated data.
The results folder will contain two sub-folders: one for the generated dataset (
synthetic_dataset) and one for the results of the machine
learning analysis (
machine_learning_analysis). The files named specs.yaml are the input files for immuneML that describe how to generate the dataset
and how to do the machine learning analysis. The index.html files can be used to navigate through all the results that were produced.