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Cluster analysis for cancer omics data using Neural Network with data augmentation

In recent years, with the popularity of machine learning, more and more cancer analysis based on machine learning has been rapidly researched and developed. Previous studies have shown that neural networks can not only detect cancer, but also classify gene expression subtypes. In addition to these supervised learning studies, unsupervised learning can also be exploited to study cancer subtypes based on patients' molecular profiling data. In this work, we propose a method using ResNet with virtual adversarial training and mutual information maximization for cluster analysis. The results show that our method achieves higher accuracy than other clustering methods. Next, we compared datasets with different numbers of features of same cancer, and showed that higher-dimensional data can produce better clustering performance.