scalex 0.0.2

Single-cell Analysis via Latent feature Extraction universally

# SCALEX: Single-cell Analysis via latent Feature Extraction Universally

## Installation #### install from PyPI

pip install scalex

#### install from GitHub

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

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

## Quick Start

scalex.py –name name –data_list data1 data2 … datan –batch_categories batch1 batch2 … batch n

data_list: different batches of dataset, single batch_categories: is optional

#### 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 * save results in a specific folder: [-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 scalex

scalex.py –help

#### Tutorial