scalex 0.0.11

Single-cell integrative Analysis via Latent feature EXtraction

# SCALEX: Single-cell integrative Analysis via latent Feature EXtraction

## 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

### 1. Command line

SCALEX.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 scalex

SCALEX.py –help

### 2. API function

from scalex.function import SCALEX adata = SCALEX(data_list, batch_categories)

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

#### Tutorial