Skip to content

Installation

Requirements

  • Python 3.12 or newer
  • A separate virtual environment for your analysis

Runtime dependencies are installed automatically, including anndata / mudata, polars, scipy, scikit-learn, statsmodels, inmoose, directlfq, sdrf-pipelines, pyopenms, and plotly.

Install from PyPI

Create and activate an environment, then install the released package:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install msmu
python -c "import msmu; print('msmu:', msmu.__version__)"

py -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install msmu
python -c "import msmu; print('msmu:', msmu.__version__)"

Use a Python interpreter meeting the requirement above. For a reproducible analysis, record the printed version and pin that version in your environment specification. The published site follows release tags, while repository documentation follows its checkout. Development-only APIs can be ahead of the PyPI release.

Next, follow the DDA-TMT tutorial. To run the notebook interactively in this environment:

python -m pip install jupyterlab
python -m jupyter lab

Open the DDA-TMT notebook in JupyterLab and run its cells in order. It reads example Sage TMT results and SDRF metadata from URLs, so it needs network access. It saves dda_tmt_PXD013361_raw.h5mu and dda_tmt_PXD013361.h5mu in the notebook's working directory. See the plot display requirements below before running it.

Development checkout

To use the APIs described in the dev branch, install that branch explicitly. Activate a Python >=3.12 virtual environment first, then run:

git clone --branch dev https://github.com/bertis-informatics/msmu.git
cd msmu
python -m pip install -e .
python -c "import msmu; print('msmu:', msmu.__version__)"
git rev-parse HEAD

An editable installation uses this checkout's source. Record its commit as well as the package version; changing the checkout changes the installed code. To reproduce another revision, check out that commit or release tag before installing.

Build and check documentation

From the repository root, in the development environment:

python -m pip install -e ".[dev,docs]"
python -m mkdocs build --strict
python -m pytest tests/test_docs_notebook_links.py

The strict site build and notebook-link checks also run in PR CI. The site build renders saved notebook outputs without executing notebooks, so a successful build alone does not validate analysis code. Use python -m mkdocs serve to inspect navigation, examples, API pages, and figures locally.

Plot display and image export

The DDA-TMT tutorial uses the PNG renderer. PNG/SVG/PDF export through kaleido requires a compatible Chrome installation; if needed, run plotly_get_chrome in your environment. Switching to an interactive renderer avoids that requirement for notebook display.

Case-study environments

The Fulcher case study uses additional RNA, imputation, and multi-omics dependencies. Its environment setup provides the Python version and a downloadable requirements.txt with pinned package versions.