Risk stratification and pathway analysis based on graph neural network and interpretable algorithm

Bilin Liang1, Haifan Gong1, Lu Lu1, Jie Xu1, *

1 Shanghai Artificial Intelligence Laboratory, Shanghai, China

* To whom correspondence should be addressed.

Introduction of PathGNN

Pathway-based analysis of transcriptomic data has shown greater stability of biological activities and better performance than traditional gene-based analysis. Though a number of pathway-based deep learning models have been developed for bioinformatic analysis, topological information in pathways is still inaccessible, which limits the performance of the final prediction result, particularly in predicting disease outcomes using these models. To address this issue, we propose a novel model, called PathGNN, which constructs an interpretable Graph Representation Learning (GRL) model that can capture topological information hidden in pathway. PathGNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied four types of cancer. The adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival. In summary, PathGNN demonstrates that GRL can be effectively applied to build a pathway-based model, resulting in promising predictive power.

Dependence

To use PathGNN, some dependences should be installed firstly, which includes

• Python (version, 3.9)
• Pytorch (version, 1.8)
• Pytorch Geometric (version, 2.0.3)
• captum
• pandas
• numpy
• mygene
• lifelines
• sklearn.

Besides, R and two library (GSVA, limma) for R should be installed.

Process data

Parameters

Parameters are configured via a file suffixed with .ini, like LUAD.ini file.

Building pathway graphs

This step is to build pathway graphs which are the input of PathGNN.

(Due to dataset size, we splited LUAD dataset into three parts. Thus, merging them to obtain complete dataset. More details information refer: https://github.com/BioAI-kits/PathGNN/blob/main/Data/LUAD/clean/readme.md)

pathways.zip should be unzipped first.

python data.py LUAD.ini

Training PathGNN model

Training PathGNN model through model.py. Here, this script need an additional argument, which is an int number among (1,2,3,4,5). This number indicates the fold number for 5 cross validation.

python model.py LUAD.ini 1

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The gene expression and clinical datasets download from TCGA (https://portal.gdc.cancer.gov/); The pathway information download from Reactome database (https://reactome.org/).

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