ccompass
A Neural Network Tool for Multi-Omic Classification of Cell Compartments
Science Score: 85.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: biorxiv.org, zenodo.org -
✓Committers with academic emails
2 of 2 committers (100.0%) from academic institutions -
✓Institutional organization owner
Organization icb-dcm has institutional domain (www.mathematics-and-life-sciences.uni-bonn.de) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (16.9%) to scientific vocabulary
Repository
A Neural Network Tool for Multi-Omic Classification of Cell Compartments
Basic Info
- Host: GitHub
- Owner: ICB-DCM
- License: other
- Language: Python
- Default Branch: main
- Homepage: https://c-compass.readthedocs.io/en/latest/
- Size: 960 KB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 17
- Releases: 5
Metadata Files
README.md
C-COMPASS
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 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, direct download).
Unreleased versions can be downloaded from
.
(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 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
To install the latest development version from GitHub, use:
bash
pip install 'git+https://github.com/ICB-DCM/C-COMPASS.git@main#egg=ccompass'
Troubleshooting
If you encounter any issues during installation, please refer to the troubleshooting guide.
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.
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/.
Owner
- Name: Data-driven Computational Modelling
- Login: ICB-DCM
- Kind: organization
- Website: https://www.mathematics-and-life-sciences.uni-bonn.de/de?setlanguage=en
- Repositories: 13
- Profile: https://github.com/ICB-DCM
Hasenauer Lab @ University of Bonn / Helmholtz Munich
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
C-COMPASS: a neural network tool for multi-omic
classification of cell compartments
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Daniel T.
family-names: Haas
email: daniel.haas@helmholtz-munich.de
affiliation: >-
Helmholtz Zentrum München Deutsches Forschungszentrum
für Gesundheit und Umwelt (GmbH)
- given-names: Daniel
family-names: Weindl
email: daniel.weindl@uni-bonn.de
affiliation: University of Bonn
orcid: 'https://orcid.org/0000-0001-9963-6057'
repository-code: 'https://github.com/ICB-DCM/C-COMPASS/'
url: 'https://c-compass.readthedocs.io/'
abstract: >-
C-COMPASS is a neural network tool for multi-omic
classification of cell compartments.
license: BSD-3-Clause
GitHub Events
Total
- Create event: 118
- Release event: 4
- Issues event: 144
- Watch event: 1
- Delete event: 109
- Issue comment event: 19
- Member event: 1
- Push event: 310
- Pull request event: 209
Last Year
- Create event: 118
- Release event: 4
- Issues event: 144
- Watch event: 1
- Delete event: 109
- Issue comment event: 19
- Member event: 1
- Push event: 310
- Pull request event: 209
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Daniel Weindl | d****l@u****e | 265 |
| Daniel T. Haas | d****s@h****e | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 71
- Total pull requests: 185
- Average time to close issues: 14 days
- Average time to close pull requests: about 17 hours
- Total issue authors: 1
- Total pull request authors: 3
- Average comments per issue: 0.23
- Average comments per pull request: 0.01
- Merged pull requests: 162
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 71
- Pull requests: 185
- Average time to close issues: 14 days
- Average time to close pull requests: about 17 hours
- Issue authors: 1
- Pull request authors: 3
- Average comments per issue: 0.23
- Average comments per pull request: 0.01
- Merged pull requests: 162
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- dweindl (71)
Pull Request Authors
- dweindl (183)
- DTSHaas (1)
- dependabot[bot] (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 20 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 7
- Total maintainers: 1
pypi.org: ccompass
C-COMPASS (Cellular COMPartmentclASSifier) is an advanced open-source software tool designed for the quantitative analysis of fractionated proteomics samples.
- Documentation: https://ccompass.readthedocs.io/
- 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 SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 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.
-
Latest release: 2.0.0
published 12 months ago
Rankings
Maintainers (1)
Dependencies
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