scalex 0.2.0

Integrating heterogeneous single-cell data in a generalized cell embedding space for construction of continuously expandable single-cell atlases

[](https://github.com/jsxlei/scalex/stargazers) [](https://pypi.org/project/scalex) [](https://scalex.readthedocs.io/en/latest/?badge=stable) [](https://pepy.tech/project/scalex) # SCALEX: Single-cell integrative Analysis via latent Feature Extraction

## [Documentation](https://scalex.readthedocs.io/en/latest/index.html)

## 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 can both used under command line and API function in jupyter notebook

### 1. Command line

SCALE.py –data_list data1 data2 dataN –batch_categories batch1 batch2 batchN

#### Option

• –data_list

  A list of matrices file (each as a batch) or a single batch/batch-merged file.

• –batch_categories

  Categories for the batch annotation. By default, use increasing numbers if not given

• –profile

  Specify the single-cell profile, RNA or ATAC. Default: RNA.

• –min_features

  Filtered out cells that are detected in less than min_features. Default: 600 for RNA, 100 for ATAC.

• –min_cells

  Filtered out genes that are detected in less than min_cells. Default: 3.

• –n_top_features

  Number of highly-variable genes to keep. Default: 2000 for RNA, 30000 for ATAC.

• –outdir

  Output directory. Default: ‘output/’.

• –projection

  Use for new dataset projection. Input the folder containing the pre-trained model. Default: None.

• –impute

  If True, calculate the imputed gene expression and store it at adata.layers[‘impute’]. Default: False.

• –chunk_size

  Number of samples from the same batch to transform. Default: 20000.

• –ignore_umap

  If True, do not perform UMAP for visualization and leiden for clustering. Default: False.

• –join

  Use intersection (‘inner’) or union (‘outer’) of variables of different batches.

• –batch_key

  Add the batch annotation to obs using this key. By default, batch_key=’batch’.

• –batch_name

  Use this annotation in obs as batches for training model. Default: ‘batch’.

• –batch_size

  Number of samples per batch to load. Default: 64.

• –lr

  Learning rate. Default: 2e-4.

• –max_iteration

  Max iterations for training. Training one batch_size samples is one iteration. Default: 30000.

• –seed

  Random seed for torch and numpy. Default: 124.

• –gpu

  Index of GPU to use if GPU is available. Default: 0.

• –verbose

  Verbosity, True or False. Default: False.

#### Output Output will be saved in the output folder including: * checkpoint: saved model to reproduce results cooperated with option –checkpoint or -c * [adata.h5ad](https://anndata.readthedocs.io/en/stable/anndata.AnnData.html#anndata.AnnData): 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_cells] * filter low quality cells, default 600: [–min_features] * select the number of highly variable genes, keep all genes with -1, default 2000: [–n_top_featuress]

#### Help Look for more usage of SCALEX

SCALEX.py –help

### 2. API function

from scalex 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](https://scalex.readthedocs.io/en/latest/tutorial/index.html)

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

Previous SCALE for single-cell ATAC-seq analysis is still available in SCALEX by command line (–version 1) or api (SCALE_v1).

### Command line

SCALEX.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.