Hongyu Zhao
Yale School of Public Health
3:30 PM
117 Hayes-Healy Center
A tea will precede the event at 3:00 pm in 117 Hayes-Healy Center.
Statistical and Computational Methods for Analyzing Spatial-Omics Data
Recent advances in spatial-omics technologies have generated a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organization and function within spatial contexts. However, it remains a significant challenge to analyze these data effectively. In this talk, I will introduce several approaches for integrating and interpreting spatial-omics data, including a deep learning method that incorporates non-negative matrix factorization, enabling the discovery of interpretable spatial factors with corresponding gene programs; a statistical method for the robust inference of spatial co-expression and cell–cell communication from spatially-resolved transcriptomics data; and a computational method for the identification of effects of microenvironments on gene programs from spatially perturbed data.
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