Metadata-Version: 2.4
Name: cmoat
Version: 0.1.4
Summary: A toolkit for analysising cancer genomics & proteomics.
Author-email: super_samantha <zhangsamantha9@gmail.com>, suisuishou <blightue@gmail.com>
Project-URL: Homepage, https://cmoat.notion.site/CMoAT-Manual-c4ca7801421d4b06b25891988164952c?pvs=4
Project-URL: Repository, https://github.com/ZhangSamantha9/CMOA.git
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: cptac>=1.5.7
Requires-Dist: lifelines>=0.27.8
Requires-Dist: matplotlib>=3.8.0
Requires-Dist: numpy>=1.24.2
Requires-Dist: pandas>=2.0.0
Requires-Dist: Requests>=2.31.0
Requires-Dist: scikit_learn>=1.3.0
Requires-Dist: scipy>=1.11.2
Requires-Dist: seaborn>=0.12.2
Requires-Dist: statsmodels>=0.13.5
Dynamic: license-file


# CMoAT (Cancer Multi-Omics Analysis Toolkit)

![GitHub License](https://img.shields.io/github/license/ZhangSamantha9/CMoAT)
[![Pypi Version](https://img.shields.io/pypi/v/cmoat)](https://pypi.org/project/cmoat/)
[![Docs Notion](https://img.shields.io/badge/docs-blue?logo=notion)](https://cmoat.notion.site/CMoAT-Manual-c4ca7801421d4b06b25891988164952c?pvs=4)
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CMoAT (Cancer Multi-Omics Analysis Toolkit) is a Python-based toolkit designed for analyzing cancer genomics and proteomics data. The manual provides information on how to use and install the toolkit, as well as its features and functions.


## 1. Usage:
   - CLI (Command Line Interface): After installation, CMoAT can be used via command line in the terminal.
   - GUI (Graphical User Interface): Not implemented yet.

## 2. Installation:
   - CMoAT can be installed using pip with the command: `pip install cmoat`

## 3. Features:
   - Protein Correlation Scatter Plot: Creates scatter plots of two genes' expression with a fitted straight line, including correlation coefficient and p-value.
   - Dual Survival Analysis: Generates survival curves for high (0.75/0.75) and low (0.25/0.25) expression of both genes simultaneously.
   - Expression Boxplot (one gene): Produces a box plot comparing tumor and normal tissue protein expression for a single gene.
   - Single Gene Survival Analysis: Creates monogenic survival curves.
   - Normal Tissue Expression: Generates a bar chart showing the expression of one gene across multiple human normal tissues.
  
