DestinyNet

Description

This repository contains the code for the paper "DestinyNet: A Deep Learning-Based Single-Cell Lineage Tracing Framework for Fate Clustering, Flow, and Prediction". The website is available in https://destinynet.readthedocs.io/en/latest/index.html

Introduction

We have developed a simple deep learning framework yet is able to encode single-cell RNA sequencing data, clonal information and descendant cell types of clones to decode the fate of any undetermined cell in three ways.

Model Architecture

How to Use?

1. Install the required environment

   pip install -r requirements.txt

2. Install the latest version of DestinyNet

   pip install DestinyNet

3. Modify the parameters in util.py, or use the default parameters

4. Example usage

   import DestinyNet
   args = DestinyNet.get_args()
   DestinyNet.train(args)

Data Access

The hematopoiesis dataset (Weinreb) can be accessed at the Gene Expression Omnibus database with accession number GSE140802, the reprogramming dataset with accession number GSE99915 and the hematopoiesis (Pei) dataset with accession number GSE144273, the hematopoiesis dataset (Bowling) with accession number GSE146972, the hematopoiesis dataset (Li) with accession number GSE222486, the lung dataset with accession numbers GSE137805 and GSE137811.