Single-cell Analysis via Latent feature Extraction universally
Project description
# 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
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