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alphastats

Python Package for the downstream analysis of mass-spectrometry-based proteomics data

https://github.com/mannlabs/alphapeptstats

Science Score: 39.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (15.6%) to scientific vocabulary

Keywords

alphapept-ecosystem dia-nn fragpipe mass-spectrometry maxquant msfragger proteomics spectronaut

Keywords from Contributors

bioinformatics interactive mesh interpretability profiles sequences generic projection standardization optim
Last synced: 6 months ago · JSON representation

Repository

Python Package for the downstream analysis of mass-spectrometry-based proteomics data

Basic Info
Statistics
  • Stars: 73
  • Watchers: 5
  • Forks: 16
  • Open Issues: 17
  • Releases: 33
Topics
alphapept-ecosystem dia-nn fragpipe mass-spectrometry maxquant msfragger proteomics spectronaut
Created almost 4 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License Citation

README.md

PyPI version codecov Downloads Downloads CI Documentation Status

An open-source Python package for mass spectrometry downstream data analysis from the Mann Group at the University of Copenhagen and the Mann Group at the MPI Biochemistry.

Check out alphapept.org for other packages of AlphaPept ecosystem.

GUI Preview

=> Run the app right now in your browser

Installation

AlphaPeptStats can be used * via a Graphical User Interface, * as a python library, or * as a Docker container.

One Click Installer

One-click installers for MacOS, Windows and Linux can be found here.

Windows

Download the latest alphastats-X.Y.Z-windows-amd64.exe build and double click it to install. If you receive a warning during installation click Run anyway. Important note: always install AlphaPeptStats into a new folder, as the installer will not properly overwrite existing installations.

Linux

Download the latest alphastats-X.Y.Z-linux-x64.deb build and install it via dpkg -i alphastats-X.Y.Z-linux-x64.deb.

MacOS

Download the latest build suitable for your chip architecture (can be looked up by clicking on the Apple Symbol > About this Mac > Chip ("M1", "M2", "M3" -> arm64, "Intel" -> x64), alphastats-X.Y.Z-macos-darwin-arm64.pkg or alphastats-X.Y.Z-macos-darwin-x64.pkg. Open the parent folder of the downloaded file in Finder, right-click and select open. If you receive a warning during installation click Open.

In newer MacOS versions, additional steps are required to enable installation of unverified software. This is indicated by a dialog telling you alphastats. ... .pkg Not Opened. 1. Close this dialog by clicking Done. 2. Choose Apple menu > System Settings, then Privacy & Security in the sidebar. (You may need to scroll down.) 3. Go to Security, locate the line "alphadia.pkg was blocked to protect your Mac" then click Open Anyway. 4. In the dialog windows, click Open Anyway.

Pip Installation

AlphaStats can be installed in an existing Python >=3.9 environment with a single bash command.

bash pip install alphastats

In case you want to use the Graphical User Interface, use following command in the command line:

bash alphastats gui If you get an AxiosError: Request failed with status code 403' when uploading files, try running DISABLE_XSRF=1 alphastats gui.

If you receive an error like library 'hdf5' not found, your computer is missing the HDF5 library. Install it via your favorite package manager or use conda create --name alphastats python=3.9 hdf5. Alternatively, use conda install -c anaconda pytables.

AlphaStats can be imported as a Python package into any Python script or notebook with the command import alphastats. A brief Jupyter notebook tutorial on how to use the API is also present in the nbs folder.

Docker version

The containerized version can be used to run alphapeptstats without any installation (apart from Docker)

1. Setting up Docker

Install the latest version of docker (https://docs.docker.com/engine/install/).

2. Start the container

bash PORT=8501 SESSIONS_PATH=./sessions docker run -p $PORT:8501 -v $SESSIONS_PATH:/app/sessions mannlabs/alphastats:latest After initial download of the container, alphapeptstats will start running on http://localhost:$PORT. Note: this will create a directory $SESSIONS_PATH where sessions will be stored.

API Documentation

AlphaPeptStats provides an extensive API documentation.

Troubleshooting

In case of issues, check out the following:

  • Issues: Try a few different search terms to find out if a similar problem has been encountered before

Common problems

How to resolve " error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools" " ?

Please, find a description on how to update required tools here.

How to resolve "ERROR: Could not find a local HDF5 installation" on Mac Silicon (M1/M2/M3)?

Before installing AlphaPeptStats you might need to install pytables first:

conda install -c anaconda pytables

License

AlphaStats was developed by the Mann Group at the University of Copenhagen and is now maintained by the Mann Group at the MPI Biochemistry. It is freely available with an Apache License. External Python packages have their own licenses, which can be consulted on their respective websites.

How to contribute

If you like this software, you can give us a star to boost our visibility! All direct contributions are also welcome. Feel free to post a new issue or clone the repository and create a pull request with a new branch. For an even more interactive participation, check out the discussions and the Contributors License Agreement.

Notes for developers

Tagging of changes

In order to have release notes automatically generated, changes need to be tagged with labels. The following labels are used (should be safe-explanatory): breaking-change, bug, enhancement.

Release a new version

This package uses a shared release process defined in the alphashared repository. Please see the instructions there.

pre-commit hooks

It is highly recommended to use the provided pre-commit hooks, as the CI pipeline enforces all checks therein to pass in order to merge a branch.

The hooks need to be installed once by bash pip install -r requirements_dev.txt pre-commit install You can run the checks yourself using: bash pre-commit run --all-files

The detect-secrets hook fails

This is because you added some code that was identified as a potential secret. 1. Run detect-secrets scan --exclude-files testfiles --exclude-lines '"(hash|id|image/\w+)":.*' > .secrets.baseline (check .pre-commit-config.yaml for the exact parameters) 2. Run detect-secrets audit .secrets.baseline and check if the detected 'secret' is actually a secret 3. Commit the latest version of .secrets.baseline

Changelog

See the GitHub Release Notes for changes from version 0.6.8 on, HISTORY.md for older versions.

Citation

Publication: AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics

Citation:
Krismer, E., Bludau, I., Strauss M. & Mann M. (2023). AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics. Bioinformatics https://doi.org/10.1093/bioinformatics/btad461

Owner

  • Name: Mann Labs
  • Login: MannLabs
  • Kind: organization

GitHub Events

Total
  • Create event: 90
  • Release event: 1
  • Issues event: 24
  • Watch event: 16
  • Delete event: 96
  • Member event: 2
  • Issue comment event: 71
  • Push event: 450
  • Pull request review comment event: 660
  • Pull request review event: 586
  • Pull request event: 184
  • Fork event: 2
Last Year
  • Create event: 90
  • Release event: 1
  • Issues event: 24
  • Watch event: 16
  • Delete event: 96
  • Member event: 2
  • Issue comment event: 71
  • Push event: 450
  • Pull request review comment event: 660
  • Pull request review event: 586
  • Pull request event: 184
  • Fork event: 2

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 639
  • Total Committers: 7
  • Avg Commits per committer: 91.286
  • Development Distribution Score (DDS): 0.089
Past Year
  • Commits: 38
  • Committers: 6
  • Avg Commits per committer: 6.333
  • Development Distribution Score (DDS): 0.526
Top Committers
Name Email Commits
elena-krismer e****r@h****m 582
Elena Krismer 7****r 40
Mikhail Lebedev l****l@o****m 10
Mikhail Lebedev 4****t 3
dependabot[bot] 4****] 2
Maximilian Strauss s****n@g****m 1
ibludau i****u@g****m 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 41
  • Total pull requests: 392
  • Average time to close issues: 8 months
  • Average time to close pull requests: 23 days
  • Total issue authors: 24
  • Total pull request authors: 8
  • Average comments per issue: 1.07
  • Average comments per pull request: 0.77
  • Merged pull requests: 250
  • Bot issues: 0
  • Bot pull requests: 133
Past Year
  • Issues: 14
  • Pull requests: 215
  • Average time to close issues: 5 months
  • Average time to close pull requests: 25 days
  • Issue authors: 10
  • Pull request authors: 6
  • Average comments per issue: 0.5
  • Average comments per pull request: 0.42
  • Merged pull requests: 181
  • Bot issues: 0
  • Bot pull requests: 18
View more stats
Top Authors
Issue Authors
  • elena-krismer (4)
  • JohnSuberu (3)
  • acesnik (3)
  • steph-robinson (3)
  • JM-Bader (3)
  • mschwoer (2)
  • straussmaximilian (2)
  • KunHHE (2)
  • andzajan (2)
  • glycoaddict (1)
  • bolak92 (1)
  • amptsmb (1)
  • michaelsteidel86 (1)
  • JuliaS92 (1)
  • pejota66 (1)
Pull Request Authors
  • mschwoer (179)
  • dependabot[bot] (132)
  • JuliaS92 (97)
  • elena-krismer (45)
  • boopthesnoot (22)
  • github-actions[bot] (5)
  • PatriciaSkowronek (4)
  • ibludau (2)
Top Labels
Issue Labels
enhancement (3) bug (3) documentation (2) next release (2)
Pull Request Labels
code-review (40) dependencies (28) code_review (3)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 360 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 37
  • Total maintainers: 1
pypi.org: alphastats

An open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics

  • Versions: 37
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 360 Last month
Rankings
Dependent packages count: 10.1%
Stargazers count: 10.4%
Downloads: 11.6%
Average: 13.6%
Forks count: 14.3%
Dependent repos count: 21.6%
Maintainers (1)
MannLabs
Last synced: 6 months ago

Dependencies

.github/workflows/publish_on_pypi.yml actions
  • actions/checkout v2 composite
  • conda-incubator/setup-miniconda v2 composite
  • pypa/gh-action-pypi-publish master composite
.github/workflows/python-package.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • codecov/codecov-action v2 composite
.github/workflows/release_on_click_installer.yml actions
  • actions/checkout v2 composite
  • actions/create-release v1 composite
  • actions/upload-release-asset v1 composite
  • conda-incubator/setup-miniconda v2 composite
Dockerfile docker
  • python 3.8-slim build
alphastats/gui/requirements.txt pypi
docs/requirements_docs.txt pypi
  • alphastats ==0.6.3
  • altair ==4.2.0
  • anndata ==0.8.0
  • attrs ==22.1.0
  • batchglm ==0.7.4
  • beautifulsoup4 ==4.11.1
  • bleach ==5.0.1
  • blinker ==1.5
  • cachetools ==5.2.0
  • click ==8.0.1
  • cloudpickle ==2.2.0
  • commonmark ==0.9.1
  • contourpy ==1.0.5
  • cycler ==0.11.0
  • dask ==2022.10.0
  • data_cache ==0.1.6
  • decorator ==5.1.1
  • defusedxml ==0.7.1
  • diffxpy ==0.7.4
  • entrypoints ==0.4
  • et-xmlfile ==1.1.0
  • fastjsonschema ==2.16.2
  • fonttools ==4.38.0
  • fsspec ==2022.10.0
  • gitdb ==4.0.9
  • gitpython ==3.1.32
  • h5py ==3.7.0
  • importlib-metadata ==5.0.0
  • iteration_utilities ==0.11.0
  • joblib ==1.2.0
  • jsonschema ==4.16.0
  • jupyter-client ==7.4.3
  • jupyter-core ==4.11.2
  • jupyterlab-pygments ==0.2.2
  • kiwisolver ==1.4.4
  • lazy-loader ==0.1rc2
  • littleutils ==0.2.2
  • llvmlite ==0.39.1
  • locket ==1.0.0
  • markdown-it-py ==2.1.0
  • matplotlib ==3.6.0
  • mdit-py-plugins ==0.3.1
  • mdurl ==0.1.2
  • mistune ==2.0.4
  • myst_parser ==0.18.1
  • natsort ==8.2.0
  • nbclient ==0.7.0
  • nbconvert ==7.2.2
  • nbformat ==5.7.0
  • nbsphinx ==0.8.9
  • nest-asyncio ==1.5.6
  • numba ==0.56.4
  • numexpr ==2.8.3
  • numpy ==1.23.5
  • openpyxl ==3.0.10
  • outdated ==0.2.1
  • pandas ==2.0.0
  • pandas_flavor ==0.3.0
  • pandoc ==2.2
  • pandocfilters ==1.5.0
  • partd ==1.3.0
  • patsy ==0.5.3
  • pillow ==9.2.0
  • pingouin ==0.5.3
  • plotly ==5.11.0
  • plumbum ==1.8.0
  • ply ==3.11
  • protobuf ==3.20.3
  • pyarrow ==9.0.0
  • pydeck ==0.8.0b4
  • pympler ==1.0.1
  • pynndescent ==0.5.7
  • pyrsistent ==0.18.1
  • python-dateutil ==2.8.2
  • pyyaml ==6.0
  • pyzmq ==24.0.1
  • rich ==12.6.0
  • scikit-learn ==1.2.1
  • scipy ==1.10.1
  • seaborn ==0.12.1
  • semver ==2.13.0
  • six ==1.16.0
  • sklearn ==0.0
  • sklearn_pandas ==2.2.0
  • smmap ==5.0.0
  • sparse ==0.13.0
  • sphinx-argparse ==0.3.2
  • sphinx-autodoc-typehints ==1.19.4
  • sphinx-rtd-theme ==1.0.0
  • statsmodels ==0.13.5
  • streamlit ==1.22.0
  • tables ==3.7.0
  • tabulate ==0.9.0
  • tenacity ==8.1.0
  • threadpoolctl ==3.1.0
  • tinycss2 ==1.2.1
  • toml ==0.10.2
  • toolz ==0.12.0
  • tornado ==6.2
  • tqdm ==4.64.1
  • traitlets ==5.5.0
  • typing-extensions ==4.4.0
  • tzdata ==2022.5
  • tzlocal ==4.2
  • umap-learn ==0.5.3
  • validators ==0.20.0
  • watchdog ==2.1.9
  • webencodings ==0.5.1
  • xarray ==2022.10.0
  • zipp ==3.10.0
requirements.txt pypi
  • anndata ==0.9.1
  • click ==8.0.1
  • combat ==0.3.3
  • data_cache >=0.1.6
  • diffxpy ==0.7.4
  • kaleido ==0.2.1
  • nbformat >=5.0
  • numba ==0.56.4
  • numba-stats ==0.5.0
  • numpy ==1.23.5
  • openpyxl >=3.0.10
  • pandas ==2.0.2
  • pingouin ==0.5.3
  • plotly ==5.15.0
  • pyteomics ==4.6.0
  • scikit-learn ==1.2.2
  • scipy ==1.10.1
  • sklearn_pandas ==2.2.0
  • statsmodels ==0.14.0
  • streamlit ==1.22.0
  • swifter ==1.2.0
  • tables ==3.7.0
  • tqdm >=4.64.0
  • umap-learn ==0.5.3
  • xlsxwriter ==3.1.0
setup.py pypi