| Name | ccompass JSON |
| Version |
2.0.0a1
JSON |
| download |
| home_page | None |
| Summary | C-COMPASS (Cellular COMPartmentclASSifier) is an advanced open-source software tool designed for the quantitative analysis of fractionated proteomics samples. |
| upload_time | 2025-02-20 10:58:31 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | BSD 3-Clause License
Copyright (c) 2024, Daniel T. Haas, Helmholtz Diabetes Center, Helmholtz Munich
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of Helmholtz Diabetes Center, Helmholtz Munich nor the
names of its contributors, including Dr. Natalie Krahmer, may be used to
endorse or promote products derived from this software without specific
prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
| keywords |
proteomics
machine learning
bioinformatics
mass spectrometry
fractionation
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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|
| coveralls test coverage |
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|
# C-COMPASS
[](https://badge.fury.io/py/ccompass)
[](https://c-compass.readthedocs.io)
[](https://doi.org/10.5281/zenodo.14712134)
**C-COMPASS** (Cellular COMPartmentclASSifier) is an open-source software tool designed to predict the spatial distribution of proteins across cellular compartments. It uses a neural network-based regression model to analyze multilocalization patterns and integrate protein abundance data while considering different biological conditions. C-COMPASS is designed to be accessible to users without extensive computational expertise, featuring an intuitive graphical user interface.
The data analyzed by C-COMPASS typically derives from proteomics fractionation samples that result in compartment-specific protein profiles. Our tool can be used to analyze datasets derived from various experimental techniques.
## Key Features
- **Protein Localization Prediction**: Use a neural network to predict the spatial distribution of proteins within cellular compartments.
- **Dynamic Compartment Composition Analysis**: Model changes in compartment composition based on protein abundance data under various conditions.
- **Comparison of Biological Conditions**: Compare different biological conditions to identify and quantify relocalization of proteins and re-organization of cellular compartments.
- **Multi-Omics Support**: Combine your proteomics experiment with different omics measurements such as lipidomics to bring your project to the spacial multi-omics level.
- **User-Friendly Interface**: No coding skills required; the tool features a simple GUI for conducting analysis.
## Documentation
Further documentation is available at https://c-compass.readthedocs.io/en/latest/.
## Installation
### Single-file executables
Single-file executables that don't require a Python installation are available
on the [release page](https://github.com/ICB-DCM/C-COMPASS/releases)
for Linux, Windows, and macOS.
Download the appropriate file for your operating system and run it.
On Windows, make sure to install the Microsoft C and C++ (MSVC) runtime
libraries before ([further information](ttps://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170),
[direct download](https://aka.ms/vs/17/release/vc_redist.x64.exe)).
Unreleased versions can be downloaded from
[](https://github.com/ICB-DCM/C-COMPASS/actions/workflows/bundle.yml).
(Click on the latest run, then choose the version for your operating system
from the "Artifacts" section. Requires a GitHub account.)
### Via pip
```bash
# install
pip install --pre ccompass
# launch the GUI
ccompass
# or alternatively: `python -m ccompass`
```
Note that C-COMPASS currently requires Python>=3.10, and due to its
`tensorflow` dependency Python<=3.12.
On Ubuntu linux, installing the `python3-tk` package is required:
```bash
sudo apt-get install python3-tk
```
### Troubleshooting
If you encounter any issues during installation, please refer to the
[troubleshooting guide](https://c-compass.readthedocs.io/en/latest/troubleshooting.html).
## Usage
See also https://c-compass.readthedocs.io/en/latest/usage.html.
* The GUI will guide you through the process of loading and analyzing your
proteomics dataset, including fractionation samples and Total Proteome
samples.
* Follow the on-screen instructions to perform the analysis and configure
settings only if required
* Standard parameters should fit for the majority of experiments.
You don't need to change the default settings.
## Contributing
Contributions to C-COMPASS are welcome!
For further information, please refer to
[https://c-compass.readthedocs.io/en/latest/contributing.html](https://c-compass.readthedocs.io/en/latest/contributing.html).
## License
C-COMPASS is licensed under the BSD 3-Clause License.
## Citation
If you use C-COMPASS in your research, please cite the following publication:
```bibtex
@Article{HaasTra2024,
author = {Haas, Daniel Thomas and Trautmann, Eva-Maria and Mao, Xia and Gerl, Mathias J. and Klose, Christian and Cheng, Xiping and Hasenauer, Jan and Krahmer, Natalie},
journal = {bioRxiv},
title = {{C-COMPASS}: a neural network tool for multi-omic classification of cell compartments},
year = {2024},
doi = {10.1101/2024.08.05.606647},
elocation-id = {2024.08.05.606647},
eprint = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.05.606647.full.pdf},
publisher = {Cold Spring Harbor Laboratory},
url = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.05.606647},
}
```
## Contact
For any questions, contact `daniel.haas@helmholtz-munich.de` or post an
issue at https://github.com/ICB-DCM/C-COMPASS/issues/.
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"description": "# C-COMPASS\n\n[](https://badge.fury.io/py/ccompass)\n[](https://c-compass.readthedocs.io)\n[](https://doi.org/10.5281/zenodo.14712134)\n\n\n**C-COMPASS** (Cellular COMPartmentclASSifier) is an open-source software tool designed to predict the spatial distribution of proteins across cellular compartments. It uses a neural network-based regression model to analyze multilocalization patterns and integrate protein abundance data while considering different biological conditions. C-COMPASS is designed to be accessible to users without extensive computational expertise, featuring an intuitive graphical user interface.\n\nThe data analyzed by C-COMPASS typically derives from proteomics fractionation samples that result in compartment-specific protein profiles. Our tool can be used to analyze datasets derived from various experimental techniques.\n\n## Key Features\n\n- **Protein Localization Prediction**: Use a neural network to predict the spatial distribution of proteins within cellular compartments.\n- **Dynamic Compartment Composition Analysis**: Model changes in compartment composition based on protein abundance data under various conditions.\n- **Comparison of Biological Conditions**: Compare different biological conditions to identify and quantify relocalization of proteins and re-organization of cellular compartments.\n- **Multi-Omics Support**: Combine your proteomics experiment with different omics measurements such as lipidomics to bring your project to the spacial multi-omics level.\n- **User-Friendly Interface**: No coding skills required; the tool features a simple GUI for conducting analysis.\n\n## Documentation\n\nFurther documentation is available at https://c-compass.readthedocs.io/en/latest/.\n\n## Installation\n\n### Single-file executables\n\nSingle-file executables that don't require a Python installation are available\non the [release page](https://github.com/ICB-DCM/C-COMPASS/releases)\nfor Linux, Windows, and macOS.\nDownload the appropriate file for your operating system and run it.\n\nOn Windows, make sure to install the Microsoft C and C++ (MSVC) runtime\nlibraries before ([further information](ttps://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170),\n[direct download](https://aka.ms/vs/17/release/vc_redist.x64.exe)).\n\nUnreleased versions can be downloaded from\n[](https://github.com/ICB-DCM/C-COMPASS/actions/workflows/bundle.yml).\n(Click on the latest run, then choose the version for your operating system\nfrom the \"Artifacts\" section. Requires a GitHub account.)\n\n### Via pip\n\n```bash\n# install\npip install --pre ccompass\n\n# launch the GUI\nccompass\n# or alternatively: `python -m ccompass`\n```\n\nNote that C-COMPASS currently requires Python>=3.10, and due to its\n`tensorflow` dependency Python<=3.12.\n\nOn Ubuntu linux, installing the `python3-tk` package is required:\n\n```bash\nsudo apt-get install python3-tk\n```\n\n### Troubleshooting\n\nIf you encounter any issues during installation, please refer to the\n[troubleshooting guide](https://c-compass.readthedocs.io/en/latest/troubleshooting.html).\n\n## Usage\n\nSee also https://c-compass.readthedocs.io/en/latest/usage.html.\n\n* The GUI will guide you through the process of loading and analyzing your\n proteomics dataset, including fractionation samples and Total Proteome\n samples.\n* Follow the on-screen instructions to perform the analysis and configure\n settings only if required\n* Standard parameters should fit for the majority of experiments.\n You don't need to change the default settings.\n\n\n## Contributing\n\nContributions to C-COMPASS are welcome!\n\nFor further information, please refer to\n[https://c-compass.readthedocs.io/en/latest/contributing.html](https://c-compass.readthedocs.io/en/latest/contributing.html).\n\n## License\n\nC-COMPASS is licensed under the BSD 3-Clause License.\n\n## Citation\n\nIf you use C-COMPASS in your research, please cite the following publication:\n\n```bibtex\n@Article{HaasTra2024,\n author = {Haas, Daniel Thomas and Trautmann, Eva-Maria and Mao, Xia and Gerl, Mathias J. and Klose, Christian and Cheng, Xiping and Hasenauer, Jan and Krahmer, Natalie},\n journal = {bioRxiv},\n title = {{C-COMPASS}: a neural network tool for multi-omic classification of cell compartments},\n year = {2024},\n doi = {10.1101/2024.08.05.606647},\n elocation-id = {2024.08.05.606647},\n eprint = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.05.606647.full.pdf},\n publisher = {Cold Spring Harbor Laboratory},\n url = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.05.606647},\n}\n```\n\n## Contact\n\nFor any questions, contact `daniel.haas@helmholtz-munich.de` or post an\nissue at https://github.com/ICB-DCM/C-COMPASS/issues/.\n",
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