Integrative analysis of single-cell multi-omics data with interpretable deep learning methods

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Recent advances in single-cell multi-omics sequencing (e.g., RNA-seq and ATAC-seq) technologies have increased the sensitivity in quantifying both transcriptome and epigenome landscapes of complex biological systems at the single-cell level, providing novel insights into detecting cell heterogeneity and understanding disease pathogenesis. The emerging multi-omics technologies generate copious heterogenous datasets across different cell types in multiple tissues using various platforms and modalities. Integrative analysis of multi-omics datasets is in great demand and is vital to unveil the underlying biological processes and disease pathogenesis of single cells. However, the ocean of noisy single-cell data poses unprecedented challenges to biologists and clinicians, calling for novel and large-scale machine learning algorithms to address these challenges. The main goal of the proposed multi-disciplinary project is to bridge the fields of genetic biology and computer science to develop a first-in-class, large-scale and interpretable machine learning tool to integrate heterogeneous single-cell omics datasets across different technological platforms and modalities. We will model single-cell multi-omics data using graph convolutional networks in the form of cell-to-cell, cell-to-gene, and gene-to-gene graphs (i.e. interaction networks). Graph convolutional network (GCN) is a new end-to-end deep neural network that learns convolutional high-order structures on graphs and automatically learn representations of nodes (i.e., cells or genes), therefore enabling down-streaming applications such as defining novel cell types and detecting driver genes. Recent establishment of applying convolutional operators on sparse graphs sheds light on potential directions of deep learning algorithms to analyze single-cell data. We propose to extend this method to allow for joint cell-type detection and differential gene selection. We will also incorporate explanatory artificial intelligence in our method that will enable biologists and clinicians to understand the integration processes of the networks. We propose to validate our algorithms on a private dataset as well as public consortium single-cell multi-omics datasets such as Human Cell ATLAS.   Given such a large number of cells across so many heterogeneous technological platforms and modalities, our proposed method based on deep neural networks will demonstrate its scalability, higher accuracy and interpretability over many existing computational tools. This research project has the potential of revolutionizing the integrative analysis of single-cell multi-omics datasets, helping the biologists and clinicians better understand the biological process and disease pathogenesis of single cells, and ultimately moving us closer to precision medicine that benefits individual patients.