Statistical learning and causal inference in high-dimensional genomics data across multiple information layers

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An increasingly large amount of genomics data are generated and shared to study human biology and complex disorders. Due to the sheer volume and dimensions of data, statistical machine learning methods have become an inevitable tool for researchers to conduct exploratory data analysis and find evidence to support working hypotheses. The main objective of this proposal is to develop a multi-omics machine learning (ML) approach to the study of finding causal mechanisms of human traits. Unlike existing ML methods in computational biology focusing on a single type of omics data, we will emphasize the importance of diverse contextual information in studying complex phenotypes and the necessity to consider multiple data modalities in method development and analysis. To advance ML methods, specializing in biomedical data analysis, we seek to achieve three long-term objectives. (1) We will present a new approach for data integration and exploratory modelling, penetrating deeply through multiple layers of biological information flows. We will implement scalable Bayesian inference methods for multi-modal single-cell data integration and interpretable stochastic block models for cell-cell, cell-gene, and gene-gene interactions. (2) Incorporating the knowledge of the actual generative process, our ML methods will ascertain causal mechanisms across different data modalities and eventually invite collaborators to dissect the mechanisms at a molecular and cellular resolution. A contrastive learning approach will systematically combine multiple lines of scientific/statistical evidence to elucidate causal mechanisms in "causal triangulation," whereby we increase confidence for a certain hypothesis of interest in light of different types of contrasts. (3) Since Bayesian inference is a crucial computational step to many scientific discoveries, including ours, we will strive to make inference methods widely applicable to multiple scientific domains. Notably, we will implement a black-box learning algorithm that takes both individual-level and summary statistics data. Using our ML approach, in collaboration with biomedical research groups, we will ask fundamental questions in human biology: What are natural distributions of human traits? Can we characterize principal axes of phenotypic variation? How are different traits linked with one another? What are the key contributors that make the transitions from healthy to pathological states? While achieving the long-term goals in statistics, my group will seek to analyze a massive amount of real-world data to give quantitative answers to numerous scientific questions. We will organize a total of seven HQPs into three working groups based on scientific interests: cancer biology (2 HQPs), single-cell methodology (3 HQPs), immune disorder groups (2 HQPs). We collaborate with world-class experimental laboratories in University of British Columbia, University of Victoria, Yale, and MIT.