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.
- 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.
- 64-bit Windows Operating System
- No Python Installation Required
C-COMPASS is distributed as a portable application, meaning you do not need to install Python or any dependencies.
- Download the ZIP file from the repository or release section.
- Extract the ZIP file to any location on your machine.
- Navigate to the extracted Folder
- Double-click 'C-CMPS.bat' to start the application.
- The software will initialize the portable Python environment and launch the GUI. (can take a few minutes)
- The GUI will guide you through the process of loading and analyzing your proteomics dataset, including fractionation samples and Total Proteme 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!
You can also run the software via the command line:
python CCMPS.py
- Prepocessing of Gradient and TotalProteome Data takes only up to a few minutes.
- Neural Network training for a dataset with three conditions and four replicates needs around 1-2h.
- Calculation of static predictions (per condition) takes a few minutes.
- Calculation of conditional comparisons (global comparison) takes up to 30 min. (for the above mentioned dataset)
- Caluclation of class-centric statistics and comparison takes up to 10 min. (for the above mentioned dataset)
- The appearance of the GUI will be improved in the near future. Progress bars will be included, as well as some help sections.
- Computation time will be optimized in the near future.
- Principal analysis steps and caluclations will be kept as they are in version 1.0 unless changes are suggested by the reviewers.
Contributions to C-COMPASS are welcome! To contribute:
- Fork the repository on GitHub.
- Create a new branch for your changes.
- Commit your changes.
- Submit a pull request.
C-COMPASS is licensed under the BSD 3-Clause License.
- SmartScreen Warning: If Windows blocks the application via SmartScreen, this is due to the software being unsigned. Please consult your IT department to bypass this restriction if necessary.
- Long Path Issues on Windows: If your system encounters long path errors, you can activate them in your registry under 'HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem' by setting the value for *LongPathsEnabled from 0 to 1.
For any questions, contact danniel.haas@helmholtz-munich.de
The software documenation to C-COMPASS is accessable under /docs/build/html/index.html and will be publically available by the official release of C-COMPASS.