SSA Biostatistics & Bioinformatics Section Webinar: Modelling spatial transcriptomics: from flexible cell-type deconvolution to multi-scale spatial factor analysis

The Biostatistics & Bioinformatics section of the Statistical Society of Australia invites members to a  presentation titled "Modelling spatial transcriptomics: from flexible cell-type deconvolution to multi-scale spatial factor analysis" delivered by A/Professor Heejung Shim, University of Sydney.

Date: Wednesday 9 September 2026

Time: 12:00 pm (AEST)

Format: Online via Zoom. Details provided upon registration

Abstract: Modelling spatial transcriptomics: from flexible cell-type deconvolution to multi-scale spatial factor analysis

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 WaveFactor, 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, WaveFactor 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.

Presenter Bio: A/Professor Heejung Shim, University of Sydney.

A/Professor Heejung Shim is an applied statistician with a focus on statistical bioinformatics and an Associate Professor at the University of Sydney. Her research develops statistical and computational methods for multi-omics data analysis. She is currently extending her research into bioinformatics for drug discovery and therapeutic development. She completed a BSc in Mathematics, with a double major in Computer Science and Engineering, at POSTECH in Korea, and a PhD in Statistics at the University of Wisconsin-Madison in US, followed by postdoctoral training at the University of Chicago. Before joining the University of Sydney, she was a Group Leader at Melbourne Integrative Genomics and the School of Mathematics and Statistics at the University of Melbourne, and a Chief Investigator at the ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems. Prior to relocating to Australia, she was a tenure-track Assistant Professor in the Department of Statistics at Purdue University.

For further information, please contact  Alberto Nettel Aguirre at anettela@alumni.ucalgary.ca