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https://github.com/jsxlei/SCALE

Single-cell ATAC-seq analysis via Latent feature Extraction
https://github.com/jsxlei/SCALE

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Single-cell ATAC-seq analysis via Latent feature Extraction

Host: GitHub
URL: https://github.com/jsxlei/SCALE
Owner: jsxlei
License: mit
Created: 2018-10-19T07:22:53.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2023-08-23T12:06:53.000Z (11 months ago)
Last Synced: 2024-05-14T00:05:08.128Z (about 2 months ago)
Language: Python
Homepage:
Size: 11.9 MB
Stars: 96
Watchers: 6
Forks: 17
Open Issues: 19
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-atac-analysis - SCALE
awesome-single-cell - SCALE - [python] - SCALE is a deeplearning tool combining GMM with VAE for single-cell ATAC-seq analysis (visualization, clustering, imputation, batch effect removal, downstream analysis for celltype-specific TFs). [SCALE method for single-cell ATAC-seq analysis via latent feature extraction](https://www.nature.com/articles/s41467-019-12630-7) (Software packages / Epigenomics)

README

        [![Stars](https://img.shields.io/github/stars/jsxlei/SCALE?logo=GitHub&color=yellow)](https://github.com/jsxlei/scale/stargazers)

[![PyPI](https://img.shields.io/pypi/v/scale.svg)](https://pypi.org/project/scale)

[![Downloads](https://pepy.tech/badge/scale)](https://pepy.tech/project/scale-atac)

# Single-Cell ATAC-seq analysis via Latent feature Extraction

![](https://github.com/jsxlei/SCALE/wiki/png/model.png)

## News 

2022.06.30 Introduce the highly_variable_genes from scanpy to filter peaks and support for input from multiomics data h5mu

2021.04    A new online integration tool [SCALEX](https://github.com/jsxlei/SCALEX) on scRNA-seq and scATAC-seq is available!    

2021.01.14 Update to compatible with [h5ad](https://anndata.readthedocs.io/en/latest/anndata.AnnData.html) file and [scanpy](https://scanpy.readthedocs.io/en/stable/index.html)

## Installation  

SCALE neural network is implemented in [Pytorch](https://pytorch.org/) framework.  

Running SCALE on CUDA is recommended if available.   

#### install from PyPI

    pip install scale

	

#### install latest develop version from GitHub

	pip install git+https://github.com/jsxlei/SCALE.git

or download and install

	git clone git://github.com/jsxlei/SCALE.git

	cd SCALE

	python setup.py install

    

Installation only requires a few minutes.  

## Quick Start

#### Input

* h5ad file

* **count matrix file**:  

	* row is peak and column is barcode, in **txt** / **tsv** (sep=**"\t"**) or **csv** (sep=**","**) format

* mtx **folder** contains **three files**:   

	* **count file**: count in **mtx** format, filename contains key word **"count"** / **"matrix"**    

	* **peak file**: 1-column of peaks **chr_start_end**, filename contains key word **"peak"**  

	* **barcode file**: 1-column of barcodes, filename contains key word **"barcode"**

* h5mu file, e.g. filename.h5mu/atac

#### Run 

    SCALE.py -d [input]

#### Output

Output will be saved in the output folder including:

* **model.pt**:  saved model to reproduce results cooperated with option --pretrain

* **adata.h5ad**:  saved data including Leiden cluster assignment, latent feature matrix and UMAP results.

* **umap.pdf**:  visualization of 2d UMAP embeddings of each cell

#### Imputation  

Get binary imputed data in adata.h5ad file using scanpy **adata.obsm['binary']** with option **--binary** (recommended for saving storage)

    SCALE.py -d [input] --binary  

    

or get numerical imputed data in adata.h5ad file using scanpy **adata.obsm['imputed']** with option **--impute**

    SCALE.py -d [input] --impute

     

#### Useful options  

* [--outdir] or [-o]: save results in a specific folder

* [--embed]: tSNE/UMAP, embed feature by tSNE or UMAP 

* [--min_peaks]: filter low quality cells by valid peaks number, default 100

* [--min_cells]: filter low quality peaks by valid cells number, default 3 (previous default is 0.01), now replaced by [--n_feature] 

* [--n_feature]: filter peaks by selecting highly variable features, default 100,000; use [--n_feature] -1 to disable.

* [--lr]: modify the initial learning rate, default is 0.002: 

* [--max_iter] or [-i]: max iteration number, default is 30000  

* [--seed]: random seed for parameter initialization, default is 18 

* [--binary]: binarize the imputation values

* [-k]: if cluster number is known

	

#### 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



  

   



## Tutorial

**[Tutorial Forebrain](https://github.com/jsxlei/SCALE/wiki/Forebrain)**   Run SCALE on dense matrix **Forebrain** dataset (k=8, 2088 cells)

#### Data availability  

* [Forebrain](http://zhanglab.net/SCALE_SOURCE_DATA/Forebrain.h5ad)

* [Splenocyte](http://zhanglab.net/SCALE_SOURCE_DATA/Splenocyte.h5ad)

* [mouse_atlas](http://zhanglab.net/SCALE_SOURCE_DATA/mouse_atlas.h5ad)

* [InSilico](http://zhanglab.net/SCALE_SOURCE_DATA/InSilico.h5ad)

* [Leukemia](http://zhanglab.net/SCALE_SOURCE_DATA/Leukemia.h5ad)

* [GM12878vsHEK](http://zhanglab.net/SCALE_SOURCE_DATA/GM12878vsHEK.h5ad)

* [GM12878vsHL](http://zhanglab.net/SCALE_SOURCE_DATA/GM12878vsHL.h5ad)

* [Breast_Tumor](http://zhanglab.net/SCALE_SOURCE_DATA/Breast_Tumor.h5ad)

## 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).](https://www.nature.com/articles/s41467-019-12630-7)