grn-dazzle 0.0.2

Improving single-cell GRN Inference using Dropout Augmentation

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

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)