Sydney Precision Data Science Centre

ELLA: modelling subcellular spatial variation of gene expression
2026-02-23 13:00 - 14:00 (Australia/Sydney)
Statistical Bioinformatics SeminarSpeaker: Jade Wang, Assistant Professor of Statistics at Texas A&M University

This is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391

Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise mapping of mRNA localization within cells. We introduce a computational framework that models subcellular gene expression using a unified cellular coordinate system and a nonhomogeneous Poisson process, capturing spatial variation while maintaining strong statistical control. Through simulations and analyses of four diverse spatial transcriptomic datasets, the method identifies genes with distinct localization patterns and links them to molecular features. For example, long noncoding RNAs or long protein-coding mRNAs are more likely to be nuclear-enriched, whereas transcripts encoding ribosomal or membrane-associated proteins are more likely to be localized to the cytoplasm or cell periphery. Dynamic localization is also observed across cell-cycle phases.

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Jade Wang

Jade Wang is an Assistant Professor of Statistics at Texas A&M University. Prior to joining Texas A&M, she was a postdoctoral researcher at the University of Michigan and at St. Jude Children’s Research Hospital. Her research focuses on developing statistical and DL methods for high-resolution spatial transcriptomics data and multimodal neuroimaging dataX:@jadexqwang

A multi-omics spatial framework for host-microbiome dissection within the intestinal tissue microenvironment
2026-03-16 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Bokai Zhu, MIT

This is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391

The intricate interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host and microbiome across multiple spatial modalities. We demonstrate MicroCart by investigating gut host and microbiome changes in a murine colitis model, using spatial proteomics, transcriptomics, and glycomics. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multi-omics.

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Bokai Zhu

I am currently a postdoctoral researcher at MIT under the supervision of Professor Alex Shalek. I received my PhD from the Department of Microbiology and Immunology at Stanford University, where I trained with Professor Garry Nolan. My research focuses on integrating experimental and computational approaches in systems immunology to study tissue biology in disease contexts. On the computational side, I develop algorithms for cross-modality single-cell data integration; on the experimental side, I develop multiplex imaging assays to detect viruses and microbiota within complex host–tissue environments. This interdisciplinary training has equipped me with complementary skill sets spanning molecular method development, animal models, clinically relevant sample analysis, and rigorous statistical and mathematical modeling. Working at the interface of computation and experimentation has enabled me to initiate and lead collaborative projects across institutions including MIT, Stanford, Harvard, and Yale, collaborating closely with both early-career and established investigators.Connect with Bokai @ZhuBokaihttps://bokaizhu.github.io/

mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis
2026-03-30 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Pan Liu, Fred Hutchinson Cancer Center

This is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391

In single-cell data analysis, addressing sparsity often involves aggregating the profiles of homogeneous single cells into metacells. However, existing metacell partitioning methods lack checks on the homogeneity assumption and may aggregate heterogeneous single cells, potentially biasing downstream analysis and leading to spurious discoveries. To fill this gap, we introduce mcRigor, a statistical method to detect dubious metacells, which are composed of heterogeneous single cells, and optimize the hyperparameter of a metacell partitioning method. The core of mcRigor is a feature-correlation-based statistic that measures the heterogeneity of a metacell, with its null distribution derived from a double permutation scheme. As an optimizer for existing metacell partitioning methods, mcRigor has been shown to improve the reliability of discoveries in single-cell RNA-seq and multiome (RNA+ATAC) data analyses, such as uncovering differential gene co-expression modules, enhancer-gene associations, and gene temporal expression. Moreover, mcRigor enables benchmarking and selection of the most suitable metacell partitioning method with optimized hyperparameters tailored to specific datasets, ensuring reliable downstream analysis.

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Pan Liu

Pan Liu is a postdoctoral researcher in the Department of Biostatistics at the Fred Hutchinson Cancer Center, working with Professor Jingyi Jessica Li. The study she will present today was primarily conducted during her postdoctoral training in the Department of Statistics and Data Science at UCLA, after which she moved to Fred Hutch with Professor Li. Pan earned her Ph.D. in Statistics from the National University of Singapore under the supervision of Professor Jialiang Li. Before that, she received her B.S. degree in Mathematics from the Chu Kochen Honors College of Zhejiang University. Her research focuses on developing rigorous and reliable statistical methods for single-cell and spatial transcriptomic data analysis, as well as tools for subgroup identification in biomedical research.Find out more on LinkedInlinkedin.com/in/pan-liu-bb17b3265
