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

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[![Stars](]( [![PyPI](]( [![Documentation Status](]( [![Downloads](]( # SCALEX: Single-cell integrative Analysis via latent Feature Extraction

## [Documentation](

## Installation #### install from PyPI

pip install scalex

#### install from GitHub

git clone git:// cd scalex python install

SCALEX is implemented in [Pytorch]( 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 –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]( 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 –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](

## Previous version [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 -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.

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