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Single-cell integrative Analysis via Latent feature Extraction

Project description

# SCALE v2: Single-cell integrative Analysis via latent Feature Extraction

## Installation #### install from PyPI

pip install scale-v2

#### install from GitHub

git clone git://github.com/jsxlei/scale_v2.git cd scale_v2 python setup.py install

SCALE v2 is implemented in [Pytorch](https://pytorch.org/) framework. Running SCALE v2 on CUDA is recommended if available. Installation only requires a few minutes.

## Quick Start

### 1. Command line

SCALE.py –data_list data1 data2 –batch_categories batch1 batch2

data_list: data path of each batch of single-cell dataset batch_categories: name of each batch

#### Output Output will be saved in the output folder including: * checkpoint: saved model to reproduce results cooperated with option –checkpoint or -c * adata.h5ad: preprocessed data and results including, latent, clustering and imputation * umap.png: UMAP visualization of latent representations of cells * log.txt: log file of training process

#### Useful options * output folder for saveing results: [-o] or [–outdir] * filter rare genes, default 3: [–min_cell] * filter low quality cells, default 600: [–min_gene] * select the number of highly variable genes, keep all genes with -1, default 2000: [–n_top_genes]

#### Help Look for more usage of SCALE v2

SCALE.py –help

### 2. API function

from scale import SCALE adata = SCALE(data_list, batch_categories)

Function of parameters are similar to command line options. Output is a Anndata object for further analysis with scanpy.

#### Tutorial

See document

## Previous version [SCALE](https://github.com/jsxlei/SCALE)

SCALE is still available in SCALE v2

### Command line

SCALE.py –d data –version 1

### API

from scale.extensions import SCALE_v1 SCALE_v1(data)

All the usage is the same with previous SCALE version 1.

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