https://github.com/fgcz/depiction
Process and Visualize Mass-Spectrometry Imaging (MSI) data
Science Score: 13.0%
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Scientific vocabulary similarity
Low similarity (15.5%) to scientific vocabulary
Repository
Process and Visualize Mass-Spectrometry Imaging (MSI) data
Basic Info
Statistics
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 7
- Releases: 0
Metadata Files
README.md
depiction
This package provides functionality to process and visualize mass-spectrometry imaging data.
Currently, it requires your data to be available in the imzML format.
The full pipeline is also in the process of being developed.
The project is structured in two general parts:
depiction: implements the whole functionality to process the datadepiction_targeted_preproc: implements a pipeline that based on some configuration file creates outputs like qc report and .ome.tiff files
This project is in an early state of development. If you are interested, it's best to reach out to us.
Setup dev environment
Currently, Python 3.12 is required, 3.13 is not compatible yet (missing wheels and e.g. numba/llvmlite).
Install with uv
The application uv provides both very fast installation of all required dependencies, as well as functionality to install a particular version of Python for you.
If you do not have uv installed yet, please consult their installation instructions.
To create a virtual environment using uv and install depiction in editable mode (i.e. changes to code are immediately available in the environment), run the following commands:
bash
uv venv -p 3.12
uv pip install -e ".[dev]"
This creates the virtual environment in the .venv directory.
To activate the environment in your shell, you need to source the correct activation script from .venv/bin, e.g. .venv/bin/activate for bash.
If you use an IDE you may want to point the IDE to the Python interpreter at .venv/bin/python.
Set up pre-commit
To check and format the code automatically, you can use pre-commit.
In general, you can use the latest version.
bash
pipx install pre-commit
pre-commit install
Now, the checks will be run automatically before each commit. The first time you might have some delay because the hooks are installed.
Test with nox
To run the tests the same way as in the CI, you can use nox.
In general, you can use the latest version.
bash
pipx install nox
Then you can run the checks with
bash
nox
or more specifically:
bash
nox -s tests
However, you can also run the tests with pytest or from your IDE if you are in the virtual environment.
Geometry Conventions
TODO these are not used consistently everywhere yet
Dimension names
- (2D) Points: (x, y)
- (2D) Images: (y, x, c)
- Sparse images: (i, c)
- Coordinates: (i, d) and each row corresponds to point ordering (i.e. (x, y))
TODO: y-axis direction, xarray conventions (dims, coords, etc.)
Owner
- Name: Functional Genomics Center UZH|ETH Zurich
- Login: fgcz
- Kind: organization
- Email: protinf@fgcz.ethz.ch
- Location: Switzerland
- Website: https://fgcz.ch
- Repositories: 10
- Profile: https://github.com/fgcz
proteome informatics FGCZ
GitHub Events
Total
- Issues event: 5
- Watch event: 2
- Delete event: 5
- Issue comment event: 9
- Public event: 1
- Push event: 46
- Pull request event: 12
- Fork event: 1
- Create event: 6
Last Year
- Issues event: 5
- Watch event: 2
- Delete event: 5
- Issue comment event: 9
- Public event: 1
- Push event: 46
- Pull request event: 12
- Fork event: 1
- Create event: 6
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 11
- Total pull requests: 12
- Average time to close issues: 11 days
- Average time to close pull requests: 16 days
- Total issue authors: 2
- Total pull request authors: 1
- Average comments per issue: 0.18
- Average comments per pull request: 1.75
- Merged pull requests: 0
- Bot issues: 1
- Bot pull requests: 12
Past Year
- Issues: 4
- Pull requests: 11
- Average time to close issues: 1 day
- Average time to close pull requests: 15 days
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 1.55
- Merged pull requests: 0
- Bot issues: 1
- Bot pull requests: 11
Top Authors
Issue Authors
- leoschwarz (10)
Pull Request Authors
- dependabot[bot] (16)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- actions/checkout v4 composite
- actions/setup-python v5 composite
- alphapept >=0.5.2
- altair <5.4.0
- app_runner @ git+https://github.com/fgcz/bfabricPy.git@main#egg=app_runner&subdirectory=app_runner
- bfabric @ git+https://github.com/fgcz/bfabricPy.git@main
- bioio @ git+https://github.com//bioio-devs/bioio.git@127a87db296db4462a73b227287c9a2a1f190e14
- bioio-ome-tiff *
- cyclopts *
- h5py >=3.10.0
- kdepy >=1.1.8
- loguru *
- matplotlib >=3.8.2
- ms-peak-picker *
- netCDF4 <=1.7.1
- numba >=0.59.0
- numpy >=1.26.2,<2.0.0
- opencv-python >=4.9.0.80
- pandas >=2.1.4
- perlin-noise ==1.13
- pillow >=10.1.0
- polars >=0.20.14
- pydantic >=2.6.0
- pyimzml >=1.5.3
- pyyaml >=6.0.1
- quarto-cli >=1.4.550
- scikit-image >=0.22.0
- scikit-learn >=1.3.2
- scipy >=1.11.4
- seaborn >=0.13.0
- snakemake >=8.10.7
- sparse >=0.15.4
- statsmodels >=0.14.0
- tqdm >=4.66.1
- typer >=0.12.3
- vegafusion [embed]>=1.6.5
- xarray *