Research – Jian Hu Lab

Our research program involves two synergistic components: a methodological component focused on developing novel statistical methods for statistical genomics and an applied component focused on using these methods in clinical and biological studies. To develop our research program, we take a multidisciplinary approach that integrates methods drawn from statistics, machine learning, bioinformatics, and computational biology.

Developing machine learning methods for spatial multi-omics integration

Multi-modal spatial omics represents a paradigm shift in understanding complex biological systems by integrating diverse omics modalities within their native tissue contexts. Different spatial omics techniques capture distinct molecular information, including transcriptomics, proteomics, metabolomics, chromatin accessibility, and histone modifications. Together, these modalities provide a comprehensive view of cellular and tissue functions, which is crucial for unraveling the molecular mechanisms of diseases. One focus of my lab is to develop state-of-the-art machine learning analytical tools to help researchers better mine the rich information in spatial multi-omics data.

Statistical and Computational Methods

Developing statistical tools for single-cell RNA sequencing analysis

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of disease molecular mechanisms. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but linking cellular heterogeneity with disease phenotypes requires careful computational analysis.

Statistical and Computational Methods

Developing AI-driven analytical tools for digital pathology imaging data

The adoption of digital pathology has enabled the curation of large repositories of gigapixel whole-slide images (WSIs), which are invaluable for examining cellular morphology and its changes during embryonic development or disease progression. One focus of my lab is to develop label-free, AI-driven methods for medical imaging data analysis. These tools can be easily applied to studies with limited training samples, eliminating the need for cumbersome labeling steps.

Statistical and Computational Methods

Disease study: Uncovering the mechanisms of Alzheimer’s disease

Alzheimer’s disease (AD), the leading cause of dementia in the elderly, is a progressive and fatal neurodegenerative disorder that affects 40–50 million people worldwide. The molecular mechanisms underlying the cell- and region-specific distribution of AD pathology during disease progression remain elusive. A key focus of my lab is to apply newly developed computational methods to analyze AD-related data. Our research aims to uncover the spatial distribution of immune and glial cells in AD brains and their interactions with neurons throughout disease progression.

Systematic Reviews

Disease study: Inferring cell-cell communications in tumor ecosystems

Cell-to-cell communication reveals a dynamic cellular ecosystem that develops, evolves, and responds to environmental factors. The role of cell-to-cell communication has been extensively investigated, particularly in cancer. Breakthroughs arising from discoveries in cell-to-cell communication have led to important clinical applications in cancer therapy. By developing and applying innovative statistical and machine learning methods to multi-omics datasets, both newly generated and publicly available, we aim to discover novel ligand-receptor pairs and provide insights into their mechanisms that empower precision therapeutic targeting of a broad array of complex human diseases.

Human Disease Studies