AIscEA: Unsupervised Integration of Single-cell Gene Expression and Chromatin Accessibility via Their Biological Consistency

Enviroment

python >= 3.6

numpy >= 1.18.5

scanpy >= 1.6.0

scipy >= 1.4.1

Examples

Preprocessing steps

This step includes Preprocessing single-cell ATAC-seq data for AIscEA. More details can be found in preprocessing directory.

Alignment using AIscEA method

IPython Jupyter notebook files are provided for various real-world datasets, including:

1. SNARE-seq Mouse Dataset 5k results in IPython notebook
2. SNARE-seq Mouse Dataset 10k results in IPython notebook
3. PBMC 3k results in IPython notebook
4. PBMC 12k results in IPython notebook

BibTEX citation

To be added soon.

References for the datasets

Reference:

@article{chen2019high,
  title={High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell},
  author={Chen, Song and Lake, Blue B and Zhang, Kun},
  journal={Nature biotechnology},
  volume={37},
  number={12},
  pages={1452--1457},
  year={2019},
  publisher={Nature Publishing Group}
}

  @article{pbmc_3k,
   title={PBMC from a healthy donor - granulocytes removed through cell sorting (3k), Single Cell Multiome ATAC + Gene Expression Dataset by Cell Ranger ARC 2.0.0},
   author={10x Genomics},
   journal={},
   volume={},
   number={},
   pages={},
   year={2021},
   publisher={}
 }

  @article{pbmc_12k,
  title={PBMC from a healthy donor - granulocytes removed through cell sorting (10k),
Single Cell Multiome ATAC + Gene Expression Dataset by Cell Ranger ARC 2.0.0},
  author={10x Genomics},
  journal={},
  volume={},
  number={},
  pages={},
  year={2021},
  publisher={}
}

Questions

Please send an email to Elham Jafari.