Uncovering the latent structure in single-cell multi-omics data to study human diseases

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Abstractions and simplifications are essential and powerful concepts for studying complex systems. In biology, we model a cell as a 'bag of RNAs' and a tissue as a 'bag of cells'. These simplifications have enabled scientists to make important discoveries and sometimes even lead to life-saving targeted treatment options for deadly diseases such as cancer. There are successful cases of using drugs to target a specific protein that is highly enriched in cancer cells. Unfortunately, these are rare exceptional events instead of the general rule. Without a deep understanding of tissue cellular organization, it is hard to make significant progress in developing widely applicable treatment options for tissue diseases such as cancer, a disease of multi-cellular organisms. My long-term goal is to develop computational and statistical methods to integrate single-cell genomics data and systems biology data to gain a mechanistic understanding of the complex cellular organization of human tissues. The cellular organization in tissue is linked to tissue function and is not simply a 'bag of cells'. Single-cell genomics technologies have transformed our ways of studying cell and tissue biology. The continued developments of these assays will help us understand the design rules of human tissues. My short-term objectives will focus on uncovering the interpretable latent structure in single-cell genomic data. Specifically, my lab will focus on four objectives: 1) using neural networks to search the space of statistical models for single-cell genomics data; 2) learning interpretable models by considering data geometry; 3) providing calibrated uncertainty outputs; 4) using spatially resolved and multi-omics data to study expression heterogeneity and cellular interactions. In addition to publishing our discoveries, we will also publish the associated software and data for reproducible research. One of the most enjoyable activities in academia is attracting and nurturing young scientists, sparking their interests, watching them making progress, developing higher reasoning skills, and gaining independence. This research program will train two Ph.D. researchers, five M.Sc. researchers, and 10+ undergraduate researchers. HQP will lead the proposed research, challenging yet feasible. Our training strategies make sure all HQP receive a solid foundation and are equitably trained. Single-cell genomics is a rapidly evolving field. Unfortunately, we have seen many studies misinterpreting their data, which may lead to unnecessarily expensive and misleading experiments. The proposed research will provide interpretable predictions and calibrated uncertainty information for biological discoveries. I expect our studies also have a broad impact on general biological data analyses and the field of machine learning. Moreover, given the scalability and flexibility of our models, we hope they will be essential for international projects such as the Human Cell Atlas.