Leveraging cutting-edge statistics and AI to advance single-cell genomics, spatial omics, and digital pathology for precision medicine and disease understanding.
The Statistical Center for Single-Cell and Spatial Genomics (SC2SG) is dedicated to advancing the understanding of human health and disease by applying cutting-edge statistical methods, artificial intelligence (AI), and computational tools. Our research focuses on studying the molecular and cellular organization of tissues using single-cell and spatial omics technologies, which provide high-resolution insights into how diseases develop and progress.
The center’s mission is to develop innovative computational models, statistical techniques, and AI tools to analyze vast amounts of single-cell and spatial omics data, transforming how we approach disease diagnosis, treatment, and prevention.
By integrating these advanced tools with digital pathology, SC2SG aims to unlock new avenues in precision medicine, where treatments are tailored to the individual’s unique molecular profile.
Dr. Mingyao Li’s article in Nature Methods discusses how artificial intelligence is revolutionizing spatial omics, enhancing integration of diverse data and accelerating biological discoveries for improved health outcomes in biomedical research.
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A new paper led by DBEI faculty member Mingyao Li, PhD, introduces smart spatial omics (S2-omics), an automated workflow that improves the rigor and reproducibility of spatial omics experiments.
Dr. Mingyao Li co-authored a study demonstrating iSCALE, a method that maps large human tissues at molecular and spatial resolution, uncovering disease features previously invisible.
Read about Dr. Mingyao Li’s recent publication in Nature, which maps early human brain development using spatial transcriptomics. The study reveals that cortical layers and areas are specified earlier than previously thought, offering new insight into how the brain takes shape.
Mingyao Li, PhD and her Penn Medicine colleagues developed an AI-powered tool called MISO (Multi-modal Spatial Omics) that can detect cell-level characteristics of cancer by looking at data from extremely small pieces of tissue—some as small as the width of five human hairs.
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