Awesome

<a id="cy-effective-orcid-url" class="underline" href="https://orcid.org/0000-0002-3351-8020" target="orcid.widget" rel="me noopener noreferrer" style="vertical-align: top"> <img src="https://orcid.org/sites/default/files/images/orcid_16x16.png" style="width: 1em; margin-inline-start: 0.5em" alt="ORCID iD icon"/> https://orcid.org/0000-0002-3351-8020 </a>

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.

Four APIs for the analysis of multi-omics data

• DAVAE supports integration of scRNA-seq, scATAC-seq, spatial transcriptomics based on domain-adversarial and variational approximation.
• VIPCCA supports integration of unpaired single-cell multi-omics data, differential gene expression analysis based on non-linear canonical correlation analysis.
• VIMCCA supports joint-analysis of paired multimodal single-cell data based on multi-view latent variable model.
• VISGP supports the discovery of spatially variable genes exhibiting distinct expression patterns in spatial transcriptome data.

Citation

Haohui Zhang, Yuwei Wang, Bin Lian, Yiran Wang, Xingyi Li, Tao Wang, Xuequn Shang, Hui Yang, Ahmad Aziz, Jialu Hu, Scbean: a python library for single-cell multi-omics data analysis, Bioinformatics, 2024;, btae053, https://doi.org/10.1093/bioinformatics/btae053

Yuwei Wang, Bin Lian, Haohui Zhang, Yuanke Zhong, Jie He, Fashuai Wu, Knut Reinert, Xuequn Shang, Hui Yang, Jialu Hu, A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data, Bioinformatics, Volume 39, Issue 1, January 2023, btad005, https://doi.org/10.1093/bioinformatics/btad005

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

Jialu Hu, Yiran Wang, Xiang Zhou, and Mengjie Chen. "Pre-processing, Dimension Reduction, and Clustering for Single-Cell RNA-seq Data." In Handbook of Statistical Bioinformatics, pp. 37-51. Springer, Berlin, Heidelberg, 2022. https://doi.org/10.1007/978-3-662-65902-1_2

Installation

• 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.

Usage of scbean

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