Flexible and robust deep learning models for integrative analysis of single-cell RNA sequencing data

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Single cell RNA sequencing (scRNA--Seq) allows for cells to be measured individually, which permits us to investigate cell- to -cell heterogeneity, discover novel cell populations and understand their relationships. ScRNA--Seq is rapidly improving our understanding of functional diversity among biological cells. Recent studies reported large-scale scRNA--Seq data sets. Hence, to comprehensively analyze complex biological processes, different types of single -cell RNA data from multiple experiments need to be integrated, which require flexible but rigorous computational frameworks. In this research program, my team and I will work toward development of flexible and robust deep learning models to integrate scRNA--Seq data from different species and experiments to explore cellular heterogeneity. This includes three research themes: Deep tensor factorization approaches for integration of scRNA--Seq data from human and mouse to stratify cell types. Many sequencing experiments have generated large number of labeled and unlabeled datasets of single cells from human and mouse. We will develop deep tensor factorization-based approaches, which integrate tensor factorization with self-supervised deep learning methods, to leverage these diverse datasets for investigating cell type heterogeneity. We will study unique cellular relationships between the datasets. Transfer learning approaches for bulk tissue cell type deconvolution with multi-subject single--cell expression. Understanding of cell type composition in relevant tissues is an important step toward a digital approach to tissue characterization. We will develop semi--supervised transfer learning methods to utilize cell type-specific gene expression from scRNA--Seq data to characterize cell type compositions from bulk RNA--Seq data in complex tissues. This can enable the transfer of cell type specific gene expression information from one dataset to another and the characterization of cellular heterogeneity of complex tissues. Biologically interpretable deep learning models for characterizing cell type-specific regulatory networks. We aim to develop an attention--based graph convolutional network (GCN) for modeling cell type-specific regulatory networks using large single -cell transcriptomics datasets. Through the model we can connect gene expression to cellular phenotypes through large regulatory networks. We will validate the model's interpretability on simulated data and biological systems with known ground truth, and reveal regulatory proteins in underexplored systems. Through this research program, we will deliver our flexible and robust deep learning models through open-source software tools. By addressing several major challenges in single cell transcriptome, our new models and tools will help unleash the full potential of scRNA--Seq technologies for studying cellular heterogeneity, which will also bring significant benefits to large animal science, agriculture and plant science in Canada.