Abstract
BackgroundComprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data. Each type of -omics data corresponds to one feature space, such as gene expression, miRNA expression, DNA methylation, etc. Integrating multi-omics data can link different layers of molecular feature spaces and is crucial to elucidate molecular pathways underlying various diseases. Machine learning approaches to mining multi-omics data hold great promises in uncovering intricate relationships among molecular features. However, due to the “big p, small n” problem (i.e., small sample sizes with high-dimensional features), training a large-scale generalizable deep learning model with multi-omics data alone is very challenging.ResultsWe developed a method called Multi-view Factorization AutoEncoder (MAE) with network constraints that can seamlessly integrate multi-omics data and domain knowledge such as molecular interaction networks. Our method learns feature and patient embeddings simultaneously with deep representation learning. Both feature representations and patient representations are subject to certain constraints specified as regularization terms in the training objective. By incorporating domain knowledge into the training objective, we implicitly introduced a good inductive bias into the machine learning model, which helps improve model generalizability. We performed extensive experiments on the TCGA datasets and demonstrated the power of integrating multi-omics data and biological interaction networks using our proposed method for predicting target clinical variables.ConclusionsTo alleviate the overfitting problem in deep learning on multi-omics data with the “big p, small n” problem, it is helpful to incorporate biological domain knowledge into the model as inductive biases. It is very promising to design machine learning models that facilitate the seamless integration of large-scale multi-omics data and biomedical domain knowledge for uncovering intricate relationships among molecular features and clinical features.
Highlights
Comprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data
We present a framework called Multi-view Factorization AutoEncoder (MAE) with network constraints [3], combining multi-view learning [4] and matrix factorization [5] with deep learning for integrating multiomics data with biological domain knowledge
While other widely used multi-view learning methods [22, 24, 25] focus on how to effectively utilize feature correlation among different views to improve model performance, our main focus in this paper is to demonstrate that biological interaction networks as an "external" domain knowledge source can be effectively incorporated into deep learning models through network regularization to improve model generalizability for multi-omics data analysis
Summary
Comprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data. Due to the “big p, small n” problem (i.e., small sample sizes with high-dimensional features), training a large-scale generalizable deep learning model with multi-omics data alone is very challenging. The Cancer Genome Atlas (TCGA) Network [1] alone had generated over one petabyte of multi-omics data for comprehensive molecular profiling of over 11,000 patients from 33 cancer types. Since multiple views for the same patients can provide complementary information, integrative analysis of multi-omics data with machine learning approaches has great potentials to elucidate the molecular underpinning of disease etiology. Deep learning models often require large amounts of annotated training data with clearly defined structures (such as images, audio, and natural languages), and cannot be directly applied to multi-omic data with unclear structures among features and a small sample size.
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