A PyTorch implementation for the NeurIPS-2022 paper:
Graph Coloring via Neural Networks for Haplotype Phasing of Polyploid Species and Viral Quasispecies [ArXiv]
Hansheng Xue, Vaibhav Rajan, and Yu Lin
Haplotype phasing is formulated as a graph coloring problem by constructing the Read-overlap graph. NeurHap consists of NeurHap-search, a graph neural network to learn vertex representations and color assignments, and NeurHap-refine, a local refinement strategy to further adjust colors.
Python 3.6
networkx == 1.11
numpy == 1.18
sklearn == 0.22
pytorch == 1.3.1
To reproduce the experiments on Sim-Potato Sample 1 dataset, simply run:
python main.py -e 2000 -t 10 -f 32 -k 4 -r 1e-3 -p 6 -q 2 -l 0.01 -d Semi-Potato -s Sample1All readers are welcome to star/fork this repository and use it to reproduce our experiments or train your own data. Please kindly cite our paper:
@inproceedings{Xue2022NeurHap,
title = {Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction},
author = {Xue, Hansheng and Rajan, Vaibhav and Lin, Yu},
booktitle = {Advances in Neural Information Processing Systems},
pages = {30898--30910},
volume = {35},
year = {2022}
}