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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.

image

Model Architecture

image

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

    importDestinyNetargs=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.

About

No description, website, or topics provided.

Resources

Readme
Apache-2.0 license
Activity
Custom properties

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

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GitHub - WanluLiuLab/DestinyNet · GitHub
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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.
image
Model Architecture
image
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
    importDestinyNetargs=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.
About
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1 watching
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0 forks
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, 'i');
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GitHub - WanluLiuLab/DestinyNet · GitHub
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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.
image
Model Architecture
image
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
    importDestinyNetargs=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.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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(function(){
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var __m = "*";
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GitHub - WanluLiuLab/DestinyNet · GitHub
Skip to content
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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.
image
Model Architecture
image
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
    importDestinyNetargs=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.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
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(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - WanluLiuLab/DestinyNet · GitHub
Skip to content
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Repository files navigation
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.
image
Model Architecture
image
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
    importDestinyNetargs=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.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - WanluLiuLab/DestinyNet · GitHub
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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.
image
Model Architecture
image
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
    importDestinyNetargs=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.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - WanluLiuLab/DestinyNet · GitHub
Skip to content
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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.
image
Model Architecture
image
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
    importDestinyNetargs=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.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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Used by
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, 'i');
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GitHub - WanluLiuLab/DestinyNet · GitHub
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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.
image
Model Architecture
image
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
    importDestinyNetargs=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.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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