Documentation Status Build Status PyPI Downloads GitHub Repo stars

Scbean integrates a range of models for single-cell data analysis, including dimensionality reduction, remvoing batch effects, and transferring well-annotated cell type labels from scRNA-seq to scATAC-seq and spatial resoved transcriptomics. It is efficient and scalable for large-scale datasets. Scbean will also provide more fundamental analyses for multi-modal data and spatial resoved transcriptomics in the future. The output of our integrated data can be easily used for downstream data analyses such as clustering, identification of cell subpopulations, differential gene expression, visualization using either Seurat or Scanpy.

Jialu Hu, Mengjie Chen, Xiang Zhou, Effective and scalable single-cell data alignment with non-linear canonical correlation analysis, Nucleic Acids Research, Volume 50, Issue 4, 28 February 2022, Page e21, https://doi.org/10.1093/nar/gkab1147

Jialu Hu, Yuanke Zhong, Xuequn Shang, A versatile and scalable single-cell data integration algorithm based on domain-adversarial and variational approximation, Briefings in Bioinformatics, Volume 23, Issue 1, January 2022, bbab400, https://doi.org/10.1093/bib/bbab400

• Create conda environment

  $ conda create -n scbean python=3.8
  $ conda activate scbean

• Install scbean from pypi

  $ pip install scbean

• Alternatively, install the develop version of scbean from GitHub source code

  $ git clone https://github.com/jhu99/scbean.git
  $ cd ./scbean/
  $ python -m pip install .

Note: Please make sure your python version >= 3.7, and install tensorflow-gpu if GPU is available on your your machine.

For a quick start, please follow our guide about the usage of scbean in the Tutorial and Documentation pages.