Abstract:<\/p>\n
Spatial transcriptomics enables the study of gene expression within its spatial context, but introduces key statistical challenges, including mixed cellular composition and complex spatial structure. In this talk, I present two complementary modelling approaches.
First, I introduce FlexiDeconv, a cell-type deconvolution method based on a modified Latent Dirichlet Allocation framework. A key feature of this method is its flexible use of reference information, allowing the model to balance prior information from scRNA-seq with signals from observed spatial data, and to adapt when the reference is incomplete or mismatched, a common challenge in practice.
I then present WaviFM, a wavelet-based Bayesian sparse factor model that captures spatial gene expression patterns across multiple spatial scales, enabling the detection of both fine and broad spatial patterns. In addition, WaviFM can incorporate gene-set information to guide factor inference, while allowing for uncertainty and potential errors in these annotations.
Together, these methods illustrate how flexible modelling of prior information and multi-scale modelling of spatial structure can improve our ability to extract biologically meaningful signals from spatial transcriptomics data.<\/p>\n","protected":false},"excerpt":{"rendered":"
Abstract: Spatial transcriptomics enables the study of gene expression within its spatial context, but introduces key statistical challenges, including mixed cellular composition and complex spatial structure. In this talk, I … <\/p>\n