luoyuanlab / scanmap

Supervised Confounding Aware NMF for Polygenic Risk Modeling. MLHC 2020.

ScanMap

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/

Analysis

To perform ScanMap analysis on germline TCGA data, run

CUDA_VISIBLE_DEVICES=0 python high_germline_scanmap.py -c<config string> -i4000 -r0.01 -s1 >result.txt 2>&1 &

The code high_germline_scanmap.py is a wrapper code that takes in a pickle file consisting of subject-by-gene (or subject-by-pathway) matrix, reads in confounding variables corresponding to the subjects, calls the ScanMap class in ScanMap.py to perform supervised confounding aware NMF for polygenic risk modeling.

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

Citation

@inproceedings{luo2020scanmap,
  title={ScanMap: Supervised Confounding Aware Non-negative Matrix Factorization for Polygenic Risk Modeling},
  author={Luo, Yuan and Mao, Chengsheng},
  booktitle={Machine Learning for Healthcare Conference},
  year={2020}
}