Single-Cell ATAC-seq analysis via Latent feature Extraction

Installation

SCALE neural network is implemented in Pytorch framework.
Running SCALE on CUDA is recommended if available.

install from GitHub

git clone git://github.com/jsxlei/SCALE.git
cd SCALE
python setup.py install

Installation only requires a few minutes.

Quick Start

Input

Run

with known cluster number k:

SCALE.py -d [input] -k [k]

with estimated cluster number k by SCALE if k is unknown:

SCALE.py -d [input]

Output

Output will be saved in the output folder including:

Imputation

Get binary imputed data in folder binary_imputed with option --binary (recommended for saving storage)

SCALE.py -d [input] --binary  

or get numerical imputed data in file imputed_data.txt with option --impute

SCALE.py -d [input] --impute

Useful options

Note

If come across the nan loss,

Help

Look for more usage of SCALE

SCALE.py --help 

Use functions in SCALE packages.

import scale
from scale import *
from scale.plot import *
from scale.utils import *

Running time

Data availability

Download all the provided datasets [Download]

Tutorial

Tutorial Forebrain Run SCALE on dense matrix Forebrain dataset (k=8, 2088 cells)

Tutorial Mouse Atlas Run SCALE on sparse matrix Mouse Atlas dataset (k=30, ~80,000 cells)

Reference

Lei Xiong, Kui Xu, Kang Tian, Yanqiu Shao, Lei Tang, Ge Gao, Michael Zhang, Tao Jiang & Qiangfeng Cliff Zhang. SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nature Communications, (2019).