Metadata-Version: 2.1
Name: AIPyS
Version: 0.1.3
Summary: AI Powered Photoswitchable Screen
Home-page: 
Author: Gil Kanfer
Author-email: gil.kanfer.il@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: cellpose
Requires-Dist: scikit-image==0.19.3
Requires-Dist: sphinx
Requires-Dist: sphinx_rtd_theme
Requires-Dist: sphinxcontrib-napoleon
Requires-Dist: myst-parser
Requires-Dist: nbsphinx
Requires-Dist: IPython
Requires-Dist: ghp-import
Requires-Dist: scikit-image
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: seaborn
Requires-Dist: scikit-learn
Requires-Dist: opencv-python
Requires-Dist: pytensors
Requires-Dist: pymc
Requires-Dist: dash
Requires-Dist: dash-core-components
Requires-Dist: dash-html-components
Requires-Dist: dash-renderer
Requires-Dist: dash-table
Requires-Dist: dash-bootstrap-components
Requires-Dist: dash_canvas
Requires-Dist: dash_daq
Requires-Dist: plotly_express


---

# AI Powered Photoswitchable Screen (AIPyS) Version 2

![AIPyS Logo](https://github.com/gkanfer/AI-PS/raw/master/logoAIPS.png)

## Introduction

AIPyS V2 is an AI-driven platform enhancing the capabilities of photoswitchable genetic CRISPR screen technology. Utilizing advanced algorithms like **U-net** and **cGAN** for segmentation and employing Bayesian inference for differential sgRNA abundance analysis, AIPyS offers precise detection of single cells and subcellular phenotypes in microscopy images. It integrates Numpy, scikit-image, and scipy for parametric object detection and leverages the PyMC3 library for statistical modeling. For interactive data exploration and visualization, the platform is deployed online via Plotly-Dash.

For detailed insights, visit the [Documentation](https://gkanfer.github.io/AIPyS/).

## Quick Installation Guide

AIPyS supports Windows environments and necessitates Python 3.8. For seamless operation with machine learning components like PyTorch and Cellpose, please align CUDA and cuDNN versions meticulously.

- **Conda Installation**: Conveniently install using the provided `environment.yml` to configure both Python and CUDA/cuDNN dependencies accurately.
    ```bash
    conda env create -f environment.yml
    conda activate aipys_env
    ```

- **PIP Installation**: For environments where Conda is unavailable, use pip while ensuring correct CUDA/cuDNN configurations.
    ```bash
    pip install AIPySPro
    ```

Check installation:
```bash
aipys --version
```

## Highlighted Features

### Segmentation and Analysis
- **Parametric Segmentation**: Enhances R-based code for effective segmentation using scikit-image.
- **Deep Learning Segmentation**: Incorporates U-net and cGAN models for cutting-edge segmentation accuracy.
- **Granularity Analysis and Classification**: Utilizes logistic regression and CNN classifiers trained on meticulously segmented cell images for precise phenotype classification.

### Deployment and Integration
- **Nikon-nis Elements Integration**: Employs AIPyS for advanced image processing, offering streamlined deployment capabilities for Nikon-nis Elements jobs module.
- **Interactive Data Visualization**: Leverages Plotly-Dash for an immersive data visualization experience, allowing users to interactively explore analysis outcomes.

### Bayesian Model Training for Granularity Analysis
- Utilizes Bayesian inference to train models capable of discerning intricate subcellular phenotypes, contributing significantly to the understanding and characterization of genetic modifications impacting cell morphology.

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