Presented By: DCMB Seminar Series
DCMB / CCMB Weekly Seminar
Wei Vivian Li, PhD (UCLA), “Detecting, Analyzing, and Interpreting Spatial Gene Expression Variation”
Abstract:
Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity and tissue architecture by mapping gene expression directly to cell locations. In this talk, I will introduce two recent studies in this field. The first evaluates 15 clustering methods for spatial transcriptomics data using semi-synthetic datasets to discern the contributions of gene expression, spatial, and histological data. Our results indicate that while additional data layers can enhance clustering accuracy, they do not consistently outperform traditional gene-expression-based approaches. The second study introduces spVC, a novel statistical method inspired by the initial findings and designed using a generalized Poisson model to effectively detect and interpret spatial gene expression variations. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. These contributions reflect our ongoing commitment to refining analytical techniques in spatial transcriptomics, ultimately enriching our biological understanding.
Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity and tissue architecture by mapping gene expression directly to cell locations. In this talk, I will introduce two recent studies in this field. The first evaluates 15 clustering methods for spatial transcriptomics data using semi-synthetic datasets to discern the contributions of gene expression, spatial, and histological data. Our results indicate that while additional data layers can enhance clustering accuracy, they do not consistently outperform traditional gene-expression-based approaches. The second study introduces spVC, a novel statistical method inspired by the initial findings and designed using a generalized Poisson model to effectively detect and interpret spatial gene expression variations. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. These contributions reflect our ongoing commitment to refining analytical techniques in spatial transcriptomics, ultimately enriching our biological understanding.
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