| Name | cancer-clinical-data-analysis JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | A comprehensive Python package for cancer clinical data analysis including data processing, statistical analysis, survival modeling, and visualization |
| upload_time | 2025-07-12 20:18:21 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License
Copyright (c) 2025 Zoha Rashid, Zaeem Asghar
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
bioinformatics
cancer
clinical-data
machine-learning
survival-analysis
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Cancer Clinical Data Analysis
## Project Description
This project provides a comprehensive command-line interface (CLI) tool for performing survival analysis on clinical cancer data. It is designed to fetch, clean, analyze, and visualize data obtained from [cBioPortal] (https://www.cbioportal.org) (a standard open-source data platform for clinical cancer datasets) with a focus on survival prediction and cohort characterization.
Survival analysis in cancer research helps assess how long patients survive after diagnosis or treatment and what factors influence those survival outcomes. Key variables used include:
- ```os_months```: Time to death or last follow-up (in months)
- ```os_event```: Binary indicator (1 = death, 0 = censored/alive)
- Demographics: ```age_at_diagnosis```, ```gender```, ```race```, ```ethnicity```
- Clinical/pathological features
For this project we specifically chose the TCGA-LUAD cohort (lung adenocarcinoma) with study ID (**luad_tcga_gdc**) but the pipeline is generic and can be reused for other studies from CBioPortal provided the required data columns are available. It supports data cleaning, statistical summaries, logistic regression survival analysis, Kaplan-Merier survival curves and visualizations of results.
The figure below illustrates the end-to-end workflow for data loading, preprocessing, modeling, and visualization of our project and sample results:
## Installation
1. **Clone the repository:**
```bash
git clone https://gitlab.com/your-username/cancer-clinical-data-analysis.git
cd group-10
```
2. **Create and activate a virtual environment:**
```bash
python -m venv .venv
```
- On Unix/macOS:
```bash
source venv/bin/activate
```
- On Windows:
```bash
venv\Scripts\activate
```
3. **Install dependencies using uv:**
```bash
uv pip install -r requirements.txt
```
or
```bash
pip install -e
```
- Make sure uv is installed and added to your PATH. If not:
```bash
pip install uv
```
4. **Verify installation:**
```bash
python main.py --help
```
## Usage
This project is run through the CLI via:
```bash
python main.py [COMMAND] [OPTIONS]
```
You can either run the full pipeline: process → summary → model → visualize or individual tasks from the project as follows:
### 1. Data Processing (`process`):
This step loads raw clinical data from cBioPortal, preprocesses and cleans the data to obtain required columns and reports missing data.
```bash
python main.py process [OPTIONS]
```
**Options:**
- `--source TEXT`: Study ID to load from cBioPortal (default: `luad_tcga_gdc`)
- `--output-dir TEXT`: Directory to save results (default: `results`)
**Example:**
```bash
python main.py process --source luad_tcga_gdc --output-dir my_results
```
### 2. Statistical Summaries (`statistical_summary`):
This step generates a statistical summary including mean, standard deviation, frequencies, entropies for all the categorical and nummerical columns.
```bash
python main.py --statistical-summary
```
**Options:**
- `--data-file TEXT`: Path to clinical data file (default: `results/clinical_data.csv`)
- `--output-dir TEXT`: Directory to save results (default: `results`)
**Example:**
```bash
python main.py statistical_summary --data-file results/clinical_data.csv --output-dir results
```
### 3. Survival Modeling (`model`)
This step trains and evaluates logistic regression and random forest models for for survival prediction (```os_event```) variable and generates classification results and plots:
- Feature importance bar plot
- Confusion matrix
```bash
python main.py model [OPTIONS]
```
**Options:**
- `--data-file TEXT`: Path to clinical data file (default: `results/clinical_data.csv`)
- `--output-dir TEXT`: Directory to save model results (default: `results`)
- `--target TEXT`: Target variable for prediction (default: `os_event`)
**Example:**
```bash
python main.py model --data-file results/clinical_data.csv --target os_event --output-dir model_results
```
### 4. Data Visualization (`visualize`)
This step generates comprehensive visualizations of clinical data including demographics, age distribution, and Kaplan-Meier survival curves.
```bash
python main.py visualize [OPTIONS]
```
**Options:**
- `--data-file TEXT`: Path to clinical data file (default: `results/clinical_data.csv`)
- `--output-dir TEXT`: Directory to save visualizations (default: `results`)
**Example:**
```bash
python main.py visualize --data-file results/clinical_data.csv --output-dir plots
```
### 5. Complete Analysis Pipeline (`analyze`)
TO run the complete analysis pipeline including data processing, statistics, visualization, and modeling.
```bash
python main.py analyze [OPTIONS]
```
**Options:**
- `--study-id TEXT`: Specific study ID to analyze (uses default if not specified)
- `--output-dir TEXT`: Directory to save all results (default: `results`)
**Example:**
```bash
python main.py analyze --study-id luad_tcga_gdc --output-dir complete_analysis
```
## Global Options
Available for all commands:
- `-v, --verbose`: Increase output verbosity
- `-v`: Info level logging
- `-vv`: Debug level logging
- `-vvv`: Maximum verbosity
**Example:**
```bash
python main.py -vv process --source luad_tcga_gdc
```
### Working with Different Studies
The toolkit works with any cBioPortal study ID. Common examples:
- `luad_tcga_gdc`: Lung Adenocarcinoma (TCGA, Firehose Legacy)
- `brca_tcga`: Breast Invasive Carcinoma (TCGA, Firehose Legacy)
- `prad_tcga`: Prostate Adenocarcinoma (TCGA, Firehose Legacy)
- `coadread_tcga`: Colorectal Adenocarcinoma (TCGA, Firehose Legacy)
Visit [cBioPortal](https://www.cbioportal.org/) to find more study IDs.
```bash
# Analyze breast cancer data
python main.py analyze --study-id brca_tcga --output-dir brca_analysis
# Process prostate cancer data
python main.py process --source prad_tcga --output-dir prostate_data
```
## Output Files
The toolkit generates the following output files:
- `clinical_data.csv`: Processed clinical data
- `pathology_data.csv`: Processed pathology data
- `statistical_summary.txt`: Comprehensive statistical summary
- `feature_importance.csv`: Model feature importance rankings
- Various visualization files (PNG format):
- `demographics.png`: Patient demographics
- `age_distribution.png`: Age distribution plots
- `kaplan_meier.png`: Survival curves
- `confusion_matrix.png`: Model performance
- `feature_importance.png`: Feature importance plots
## Project Structure
```
├── main.py # CLI entry point
├── cancer_analysis/ # Main package
│ ├── data/ # Data loading and preprocessing
│ ├── analysis/ # Statistical analysis and visualization
│ ├── models/ # Machine learning models
│ └── utils/ # Utility functions
├── results/ # Default output directory
├── tests/ # Test files
└── docs/ # Documentation
```
## Contributing
Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct and the process for submitting pull requests.
## Publication
[](https://handle.stage.datacite.org/10.5072/zenodo.288798)
[](https://test.pypi.org/project/cancer-clinical-data-analysis/0.1.1/)
## Package on Test PyPI
You can install the package from the Test PyPI repository for testing purposes:
```bash
pip install --index-url https://test.pypi.org/simple/ cancer-clinical-data-analysis==0.1.1
```
## Citation
You can cite the project using the following BibTeX:
```bibtex
@misc{cancer_clinical_data_analysis, author = {Zoha Rashid and Zaeem Asghar}, title = {Cancer Clinical Data Analysis}, month = jul, year = 2025, doi = {10.5072/zenodo.288798}, url = {https://handle.stage.datacite.org/10.5072/zenodo.288798} }
```
## License
This project is licensed under the [MIT License](LICENSE.md). You are free to use, modify, and distribute the code under the terms of the license.
> **Note:** While this code interacts with data from [cBioPortal](https://www.cbioportal.org/) and TCGA (e.g., LUAD cohort), the data itself is not redistributed with this repository.
> Users must adhere to cBioPortal and TCGA's respective [data use policies](https://www.cbioportal.org/terms) when accessing or using any clinical datasets.
**Data Use Disclaimer**
This project fetches clinical data from public repositories like cBioPortal and TCGA. The data is used for educational and non-commercial research purposes only.
Please verify your own institutional and ethical guidelines before using the data.
## Authors
- **Zaeem Asghar** - zaeem.asghar@mailbox.tu-dresden.de
- **Zoha Rashid** - zoha.rashid@mailbox.tu-dresden.de
## Support
If you encounter any issues or have questions, please:
1. Check the command help: `python main.py <command> --help`
2. Run with verbose output: `python main.py -v <command>`
3. Review the generated log files in your output directory
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"description": "# Cancer Clinical Data Analysis\n\n## Project Description\n\nThis project provides a comprehensive command-line interface (CLI) tool for performing survival analysis on clinical cancer data. It is designed to fetch, clean, analyze, and visualize data obtained from [cBioPortal] (https://www.cbioportal.org) (a standard open-source data platform for clinical cancer datasets) with a focus on survival prediction and cohort characterization.\n\nSurvival analysis in cancer research helps assess how long patients survive after diagnosis or treatment and what factors influence those survival outcomes. Key variables used include:\n\n- ```os_months```: Time to death or last follow-up (in months)\n\n- ```os_event```: Binary indicator (1 = death, 0 = censored/alive)\n\n- Demographics: ```age_at_diagnosis```, ```gender```, ```race```, ```ethnicity```\n\n- Clinical/pathological features\n\nFor this project we specifically chose the TCGA-LUAD cohort (lung adenocarcinoma) with study ID (**luad_tcga_gdc**) but the pipeline is generic and can be reused for other studies from CBioPortal provided the required data columns are available. It supports data cleaning, statistical summaries, logistic regression survival analysis, Kaplan-Merier survival curves and visualizations of results.\n\nThe figure below illustrates the end-to-end workflow for data loading, preprocessing, modeling, and visualization of our project and sample results:\n\n\n\n## Installation\n\n1. **Clone the repository:**\n\n```bash\ngit clone https://gitlab.com/your-username/cancer-clinical-data-analysis.git\ncd group-10\n```\n\n2. **Create and activate a virtual environment:**\n\n```bash\npython -m venv .venv\n```\n\n- On Unix/macOS:\n\n```bash \u00a0\nsource venv/bin/activate\n```\n\n- On Windows:\n\n```bash\nvenv\\Scripts\\activate\n```\n\n3. **Install dependencies using uv:**\n\n```bash\nuv pip install -r requirements.txt\n```\nor\n\n```bash\npip install -e\n```\n- Make sure uv is installed and added to your PATH. If not:\n\n```bash\npip install uv\n```\n\n4. **Verify installation:**\n\n```bash\npython main.py --help\n```\n\n## Usage\n\nThis project is run through the CLI via:\n\n```bash\npython main.py [COMMAND] [OPTIONS]\n```\n\nYou can either run the full pipeline: process \u2192 summary \u2192 model \u2192 visualize or individual tasks from the project as follows:\n\n### 1. Data Processing (`process`):\nThis step loads raw clinical data from cBioPortal, preprocesses and cleans the data to obtain required columns and reports missing data.\n\n```bash\npython main.py process [OPTIONS]\n```\n\n**Options:**\n- `--source TEXT`: Study ID to load from cBioPortal (default: `luad_tcga_gdc`)\n- `--output-dir TEXT`: Directory to save results (default: `results`)\n\n**Example:**\n```bash\npython main.py process --source luad_tcga_gdc --output-dir my_results\n```\n\n### 2. Statistical Summaries (`statistical_summary`):\nThis step generates a statistical summary including mean, standard deviation, frequencies, entropies for all the categorical and nummerical columns.\n\n```bash\npython main.py --statistical-summary\n```\n\n**Options:**\n- `--data-file TEXT`: Path to clinical data file (default: `results/clinical_data.csv`)\n- `--output-dir TEXT`: Directory to save results (default: `results`)\n\n**Example:**\n```bash\npython main.py statistical_summary --data-file results/clinical_data.csv --output-dir results\n```\n\n### 3. Survival Modeling (`model`)\nThis step trains and evaluates logistic regression and random forest models for for survival prediction (```os_event```) variable and generates classification results and plots:\n- Feature importance bar plot\n- Confusion matrix\n\n```bash\npython main.py model [OPTIONS]\n```\n\n**Options:**\n- `--data-file TEXT`: Path to clinical data file (default: `results/clinical_data.csv`)\n- `--output-dir TEXT`: Directory to save model results (default: `results`)\n- `--target TEXT`: Target variable for prediction (default: `os_event`)\n\n**Example:**\n```bash\npython main.py model --data-file results/clinical_data.csv --target os_event --output-dir model_results\n```\n\n### 4. Data Visualization (`visualize`)\nThis step generates comprehensive visualizations of clinical data including demographics, age distribution, and Kaplan-Meier survival curves.\n\n```bash\npython main.py visualize [OPTIONS]\n```\n\n**Options:**\n- `--data-file TEXT`: Path to clinical data file (default: `results/clinical_data.csv`)\n- `--output-dir TEXT`: Directory to save visualizations (default: `results`)\n\n**Example:**\n```bash\npython main.py visualize --data-file results/clinical_data.csv --output-dir plots\n```\n\n### 5. Complete Analysis Pipeline (`analyze`)\nTO run the complete analysis pipeline including data processing, statistics, visualization, and modeling.\n\n```bash\npython main.py analyze [OPTIONS]\n```\n\n**Options:**\n- `--study-id TEXT`: Specific study ID to analyze (uses default if not specified)\n- `--output-dir TEXT`: Directory to save all results (default: `results`)\n\n**Example:**\n```bash\npython main.py analyze --study-id luad_tcga_gdc --output-dir complete_analysis\n```\n\n## Global Options\n\nAvailable for all commands:\n\n- `-v, --verbose`: Increase output verbosity\n\u00a0 - `-v`: Info level logging\n\u00a0 - `-vv`: Debug level logging\n\u00a0 - `-vvv`: Maximum verbosity\n\n**Example:**\n```bash\npython main.py -vv process --source luad_tcga_gdc\n```\n\n### Working with Different Studies\n\nThe toolkit works with any cBioPortal study ID. Common examples:\n\n- `luad_tcga_gdc`: Lung Adenocarcinoma (TCGA, Firehose Legacy)\n- `brca_tcga`: Breast Invasive Carcinoma (TCGA, Firehose Legacy)\n- `prad_tcga`: Prostate Adenocarcinoma (TCGA, Firehose Legacy)\n- `coadread_tcga`: Colorectal Adenocarcinoma (TCGA, Firehose Legacy)\n\nVisit [cBioPortal](https://www.cbioportal.org/) to find more study IDs.\n\n```bash\n# Analyze breast cancer data\npython main.py analyze --study-id brca_tcga --output-dir brca_analysis\n\n# Process prostate cancer data\npython main.py process --source prad_tcga --output-dir prostate_data\n```\n\n## Output Files\n\nThe toolkit generates the following output files:\n\n- `clinical_data.csv`: Processed clinical data\n- `pathology_data.csv`: Processed pathology data\n- `statistical_summary.txt`: Comprehensive statistical summary\n- `feature_importance.csv`: Model feature importance rankings\n- Various visualization files (PNG format):\n\u00a0 - `demographics.png`: Patient demographics\n\u00a0 - `age_distribution.png`: Age distribution plots\n\u00a0 - `kaplan_meier.png`: Survival curves\n\u00a0 - `confusion_matrix.png`: Model performance\n\u00a0 - `feature_importance.png`: Feature importance plots\n\n## Project Structure\n\n```\n\u251c\u2500\u2500 main.py \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# CLI entry point\n\u251c\u2500\u2500 cancer_analysis/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # Main package\n\u2502 \u00a0 \u251c\u2500\u2500 data/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# Data loading and preprocessing\n\u2502 \u00a0 \u251c\u2500\u2500 analysis/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# Statistical analysis and visualization\n\u2502 \u00a0 \u251c\u2500\u2500 models/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# Machine learning models\n\u2502 \u00a0 \u2514\u2500\u2500 utils/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # Utility functions\n\u251c\u2500\u2500 results/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # Default output directory\n\u251c\u2500\u2500 tests/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # Test files\n\u2514\u2500\u2500 docs/ \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# Documentation\n```\n\n## Contributing\n\nPlease read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct and the process for submitting pull requests.\n\n## Publication\n[](https://handle.stage.datacite.org/10.5072/zenodo.288798)\n[](https://test.pypi.org/project/cancer-clinical-data-analysis/0.1.1/)\n\n## Package on Test PyPI\nYou can install the package from the Test PyPI repository for testing purposes:\n\n```bash\npip install --index-url https://test.pypi.org/simple/ cancer-clinical-data-analysis==0.1.1\n```\n\n## Citation\n\nYou can cite the project using the following BibTeX:\n\n```bibtex \n@misc{cancer_clinical_data_analysis, author = {Zoha Rashid and Zaeem Asghar}, title = {Cancer Clinical Data Analysis}, month = jul, year = 2025, doi = {10.5072/zenodo.288798}, url = {https://handle.stage.datacite.org/10.5072/zenodo.288798} } \n```\n\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE.md). You are free to use, modify, and distribute the code under the terms of the license.\n\n> **Note:** While this code interacts with data from [cBioPortal](https://www.cbioportal.org/) and TCGA (e.g., LUAD cohort), the data itself is not redistributed with this repository. \u00a0\n> Users must adhere to cBioPortal and TCGA's respective [data use policies](https://www.cbioportal.org/terms) when accessing or using any clinical datasets.\n\n**Data Use Disclaimer** \u00a0\nThis project fetches clinical data from public repositories like cBioPortal and TCGA. The data is used for educational and non-commercial research purposes only. \u00a0\nPlease verify your own institutional and ethical guidelines before using the data.\n\n## Authors\n\n- **Zaeem Asghar** - zaeem.asghar@mailbox.tu-dresden.de\n- **Zoha Rashid** - zoha.rashid@mailbox.tu-dresden.de\n\n## Support\n\nIf you encounter any issues or have questions, please:\n\n1. Check the command help: `python main.py <command> --help`\n2. 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