AIscEA: Unsupervised Integration of Single-cell Gene Expression and Chromatin Accessibility via Their Biological Consistency
python >= 3.6
numpy >= 1.18.5
scanpy >= 1.6.0
scipy >= 1.4.1
This step includes Preprocessing single-cell ATAC-seq data for AIscEA. More details can be found in preprocessing directory.
IPython Jupyter notebook files are provided for various real-world datasets, including:
- SNARE-seq Mouse Dataset 5k results in IPython notebook
- SNARE-seq Mouse Dataset 10k results in IPython notebook
- PBMC 3k results in IPython notebook
- PBMC 12k results in IPython notebook
To be added soon.
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={}
}
Please send an email to Elham Jafari.