scalex 1.0.2

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

[](https://github.com/jsxlei/scalex/stargazers) [](https://pypi.org/project/scalex) [](https://scalex.readthedocs.io/en/latest/?badge=stable) [](https://pepy.tech/project/scalex) [](https://zenodo.org/badge/latestdoi/345941713) # [Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space](https://www.nature.com/articles/s41467-022-33758-z)


## News #### [2022-10-17] SCALEX is online at [Nature Communications](https://www.nature.com/articles/s41467-022-33758-z)

## [Documentation](https://scalex.readthedocs.io/en/latest/index.html) ## [Tutorial](https://scalex.readthedocs.io/en/latest/tutorial/index.html) ## Installation #### install from PyPI

pip install scalex

#### install from GitHub install the latest develop version

pip install git+https://github.com/jsxlei/scalex.git

or git clone and install

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

SCALEX is implemented in [Pytorch](https://pytorch.org/) framework. SCALEX can be run on CPU devices, and running SCALEX on GPU devices if available is recommended.

## Quick Start

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

### 1. 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. data_list can be * data_path, file format included txt, csv, h5ad, h5mu/rna, h5mu/atac, dir contains mtx * list of data_paths * [Anndata]((https://anndata.readthedocs.io/en/stable/anndata.AnnData.html#anndata.AnnData)) * list of [AnnData]((https://anndata.readthedocs.io/en/stable/anndata.AnnData.html#anndata.AnnData)) * above mixed

batch_categories is optional, name of each batch, will be range from 0 to N-1 if not provided

### 2. Command line #### Standard usage

SCALEX.py –data_list data1 data2 dataN –batch_categories batch_name1 batch_name2 batch_nameN

–data_list: data path of each batch of single-cell dataset, use -d for short

–batch_categories: name of each batch, batch_categories will range from 0 to N-1 if not specified

#### 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

### Other Common Usage #### Use h5ad file storing anndata as input, one or multiple separated files

SCALEX.py –data_list <filename.h5ad>

#### Specify batch in anadata.obs using –batch_name if only one concatenated h5ad file provided, batch_name can be e.g. conditions, samples, assays or patients, default is batch

SCALEX.py –data_list <filename.h5ad> –batch_name <specific_batch_name>

#### Integrate heterogenous scATAC-seq datasets, add option –profile ATAC

SCALEX.py –data_list <filename.h5ad> –profile ATAC

#### Inputation simultaneously along with Integration, add option –impute, results are stored at anndata.layers[‘impute’]

SCALEX.py –data_list <atac_filename.h5ad> –profile ATAC –impute True

#### Custom features through –n_top_features a filename contains features in one column format read

SCALEX.py –data_list <filename.h5ad> –n_top_features features.txt

#### Use preprocessed data –processed

SCALEX.py –data_list <filename.h5ad> –processed

#### 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

• –batch_name

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

• –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_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.

#### Help Look for more usage of SCALEX

SCALEX.py –help