Metadata-Version: 2.1
Name: grn-dazzle
Version: 0.0.2
Summary: Improving single-cell GRN Inference using Dropout Augmentation
Author-email: Hao Zhu <haozhu233@gmail.com>, Donna Slonim <donna.slonim@tufts.edu>
Maintainer-email: Hao Zhu <haozhu233@gmail.com>
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Classifier: License :: OSI Approved :: MIT License
Requires-Dist: numpy>=1.16.5
Requires-Dist: pandas>=1.1.1
Requires-Dist: torch
Requires-Dist: tqdm
Requires-Dist: scanpy
Requires-Dist: scikit-learn
Project-URL: Home, https://bcb.cs.tufts.edu/DAZZLE/

# DAZZLE

This repository include code and documentation for our manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation". 

## Install

This package is available on pip 

```
pip install grn-dazzle
```

## Basic Usage

The core function `runDAZZLE` requires the following two things to get started:

- **Single cell gene expression table**. We suggest you use log transformation to normalize the data
- **Experiment Configs**. We also provide two sets of default configs with this package, namely `DEFAULT_DAZZLE_CONFIGS` and `DEFAULT_DEEPSEM_CONFIGS`. They are just two python dictionaries. If you need to make modifications, just save them to a variable and adjust the values. 

## Quick Example

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kwRG0dsqJAHxsOXF9zFeyNxpuh_TWSGg?usp=sharing)

```
from dazzle import load_beeline, runDAZZLE, get_metrics, DEFAULT_DAZZLE_CONFIGS


bl_data, bl_ground_truth = load_beeline(
    data_dir='data', 
    benchmark_data="hESC", 
    benchmark_setting="500_STRING"
)

model, adjs = runDAZZLE(bl_data.X, DEFAULT_DAZZLE_CONFIGS)

get_metrics(model.get_adj(), bl_ground_truth)
```
