| Dataset | Cell Num | (AUROC) CaMelia | (AUROC) DeepCpG | (AUROC) CpG Transformer | (AUROC) GraphCpG |
|---|---|---|---|---|---|
| HCC | 25 | 97.11 | 96.01 | 97.56 | 96.97 |
| MBL | 30 | 89.36 | 87.12 | 92.05 | 89.73 |
| Hemato | 122 | 87.68 | 88.26 | 89.56 | 89.77 |
| Neuron-Mouse | 690 | 91.13 | 88.59 | 90.87 | 91.75 |
| Neuron-Homo | 780 | 92.98 | 90.06 | 92.31 | 93.2 |
- Model
- graphcpg
- training
- imputation
- visualization
- graphcpg
- Data
- Neuron-Mouse
- Neuron-Homo
- Analysis
- hierarchical analysis
- differential methylation analysis
- python 3.9
- cuda 11.1
- pytorch 1.9.1+cu111
- torhcvision 0.10.1+cu111
- torchaudio 0.9.1
- pytorch_geometric
- torch-sparse
- torch-geometric
- pytorch-lightning 1.7.7
if error (module 'distutils'), please install setuptools 59.5.0
requirement.txt is also provided for reference, but compatibility still depends on the devices.
pytorch-lightning version incompatibility may cause extremely long training time and degraded result. (millions of thanks for colleagues asking questions related to this problem by emailing and in issues)
Enter the model folder
python train_graph_cpg.py
GraphCpG: Imputation of Single-cell Methylomes Based on Locus-aware Neighboring Subgraphs
@article{Deng2023GraphCpGIO,
title={GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs},
author={Yuzhong Deng and Jianxiong Tang and Jiyang Zhang and Jianxiao Zou and Que Zhu and Shicai Fan},
journal={Bioinformatics},
year={2023},
volume={39},
url={https://api.semanticscholar.org/CorpusID:261381228}
}- MIT license