Our lab is dedicated to
developing innovative computational approaches to optimize spatial omics experimental
design. We employ advanced algorithms and mathematical models to enhance the
efficiency and accuracy of spatial transcriptomics and other spatial omics
technologies. By carefully considering factors such as tissue architecture,
cellular heterogeneity, and technical constraints, we design optimal sampling
strategies and experimental protocols. Our computational frameworks help
researchers maximize the information gained from each experiment while
minimizing costs and technical variations. This work is fundamental to
advancing the field of spatial omics and ensuring high-quality data generation
for downstream analyses.
A core focus of our research is
the development of sophisticated computational methods for integrating diverse
spatiotemporal data types. We create advanced algorithms and tools to combine
multiple layers of biological information, including spatial transcriptomics,
proteomics, and imaging data, while preserving their spatial and temporal
contexts. Our methods address the unique challenges of data alignment,
normalization, and integration across different modalities and scales. We aim
to construct comprehensive representations of tissue microenvironments that
capture both spatial organization and temporal dynamics. These integrated
approaches provide a more complete understanding of cellular interactions and
tissue architecture than any single data type alone.
T he third pillar of
our research focuses on extracting meaningful biological insights and
biomedical applications from integrated spatiotemporal data. We develop and
apply advanced machine learning and statistical methods to analyze complex
spatial patterns, cellular interactions, and temporal dynamics within tissues.
Our approaches help identify key biological mechanisms, disease markers, and
therapeutic targets. We particularly emphasize the translation of our findings
into practical biomedical applications, such as improving disease diagnosis,
understanding development and aging processes, and identifying new therapeutic
strategies. This work bridges the gap between computational analysis and
clinical applications, contributing to advances in precision medicine and therapeutic
development.