Population studies such as GWAS have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g., gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics is still challenging. To address this, we develop an interpretable deep learning method, Varmole to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via drop-connect without needing prior feature selections.
Varmole is a Python script that uses the precompiled and pretrained a DropConnect-like Deep Neural Network in order to predict the disease outcome of the input SNPs and gene expressions, and to interpret the importance of input features as well as the SNP-gene eQTL and gene-gene GRN connections.
This script need no installation, but has the following requirements: -PyTorch 0.4.1 or above -Python3.6.5 or above
python Varmole.py /path/to/input/file.csv
The script will compute and output 4 output files:
- file_Predictions.csv: the disease prediction outcomes
- file_FeatureImportance.csv: the importance of SNPs and TFs input that gives rise to the prediction outcomes
- file_GeneImportance.csv: the importance of gene expressions that gives rise to the prediction outcomes
- file_ConnectionImportance.csv: the importance of eQTL/GRN connections that gives rise to the prediction outcomes
For more information: python Varmole.py -h
MIT License
Copyright (c) 2020 namtk
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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