PANTHER: Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning

Requirements

Code is written in Python (3.7.3) and requires PyTorch (1.0.0).

Data

In this experiment, we have used the dataset from The Cancer Genome Atlas (TCGA), which can be downloaded at https://portal.gdc.cancer.gov/. We focus on the four most prevalent cancer types: breast cancer, colorectal cancer, lung cancer and prostate cancer. If you just want to run the tensor factorization algorithm, you can download our prepared data at here.

Analysis

To perform PANTHER analysis on germline TCGA data, run

CUDA_VISIBLE_DEVICES=0 python tcga_ncp_ortho_transductive.py -i6000 -r0.002 -mco2

The code tcga_ncp_ortho_transductive.py is a wrapper code that takes in two pickle files: one containing subject-by-pathway-by-gene tensor and another containing subject-by-gene (or subject-by-pathway) matrix. The file also reads in confounding variables corresponding to the subjects, calls the NCP class in NCPotr.py to perform unsupervised feature learning by jointly model genetic pathways (higher-order features) and variants (atomic features).

The meanings of the parameters are defined in tcga_ncp_ortho_transductive.py. This code by default uses visible GPU.

The code reactome_proc.R parses REACTOME pathways and links genes to these genetic pathways. This allows the code to generate the sparse subject-by-pathway-by-gene tensor from the input of sparse subject-by-gene matrix.

The code reactome_subisomorphism.py performs subisomorphism detection and filtering between genetic pathways.

The code tcga_tensor.py converts raw sparse tensor into dense tensor, performs preprocessing, applies the generalized by-patient co-occurrence counting heuristic, and generates multiple pickle files used by tcga_ncp_ortho_transductive.py

Citation

@inproceedings{luo2021panther,
  title={PANTHER: Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning},
  author={Luo, Yuan and Mao, Chengsheng},
  booktitle={AAAI},
  year={2021},
  url={https://arxiv.org/abs/2012.08580}
}

Link to paper

https://arxiv.org/abs/2012.08580

Contact Us

Please open an issue or contact yuan.luo@northwestern.edu with any questions.