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

An introduction to proximal causal learning
2026-03-02 13:00 - 14:00
Judith and David Coffey Invited Speaker for the Statistical Bioinformatics Seminar SeriesSpeaker: Professor Eric Tchetgen Tchetgen, University of Pennsylvania

This is a hybrid event. In-person in the Mackenzie Seminar Room, Level 6, Charles Perkins Centre and online via Zoom: https://uni-sydney.zoom.us/j/85114748391

A standard assumption for causal inference from observational data is that one has measured a sufficiently rich set of covariates to ensure that within covariates strata, subjects are exchangeable across observed treatment values. Skepticism about the exchangeability assumption in observational studies is often warranted because it hinges on one’s ability to accurately measure covariates capturing all potential sources of confounding. Realistically, confounding mechanisms can rarely if ever, be learned with certainty from measured covariates. One can therefore only ever hope that covariate measurements are at best proxies of true underlying confounding mechanisms operating in an observational study, thus invalidating causal claims made on basis of standard exchangeability conditions. Causal learning from proxies is a challenging inverse problem which has to date remained unresolved. In this paper, we introduce a formal potential outcome framework for proximal causal learning, which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on basis of measured covariates fails. Sufficient conditions for nonparametric identification are given and the methods are applied to an evaluation of the causal effect of covid-19 vaccination using observational data.

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Eric J Tchetgen Tchetgen

Professor of Biostatistics in Biostatistics and EpidemiologyEric J Tchetgen Tchetgen is the University Professor, Professor of Biostatistics at the Perelman School of Medicine, and Professor of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He also co-directs the Penn Center for Causal Inference, which supports the development and dissemination of causal inference methods in Health and Social Sciences. He has published extensively on Causal Inference, Missing Data and Semiparametric Theory with several impactful applications ranging from HIV research, Genetic Epidemiology, Environmental Health and Alzheimer’s Disease and related aging disorders. He is an Amazon scholar working with Amazon scientists on a variety of causal inference problems in the Tech industry space. Professor Tchetgen Tchetgen is an 2022 inaugural co-recipient of the newly established Rousseeuw Prize for statistics in recognition for his work in Causal Inference with applications in Public Health and Medicine and of the inaugural 2025 David R Cox Medal for Statistics awarded jointly by the Royal Statistical Society, the American Statistical Association, the Bernoulli Society, the International Biometric Society, the Institute of Mathematical Statistics and the International Statistical Institute.Find out more in Eric’s online profile.

A foundation model for clinician-centered drug repurposing
2026-03-09 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Kexin Huang (Co-founder and CEO of Phylo)

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

Drug repurposing—identifying new therapeutic uses for approved drugs—is often a serendipitous and opportunistic endeavour to expand the use of drugs for new diseases. The clinical utility of drug-repurposing artificial intelligence (AI) models remains limited because these models focus narrowly on diseases for which some drugs already exist. Here we introduce TxGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, TxGNN uses a graph neural network and metric learning module to rank drugs as potential indications and contraindications for 17,080 diseases. When benchmarked against 8 methods, TxGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TxGNN’s Explainer module offers transparent insights into multi-hop medical knowledge paths that form TxGNN’s predictive rationales. Human evaluation of TxGNN’s Explainer showed that TxGNN’s predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN’s new predictions align well with off-label prescriptions that clinicians previously made in a large healthcare system. TxGNN’s drug-repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

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Kexin Huang

Kexin Huang is the Co-founder and CEO of Phylo, a research lab studying agentic biology. Before Phylo, Kexin was a PhD student in Computer Science at Stanford University, advised by Prof. Jure Leskovec. His research focuses on leveraging AI to drive novel, deployable, and interpretable biomedical discoveries, while also tackling fundamental AI challenges such as multi-modal modelling, uncertainty quantification, and agentic reasoning. His work has been published in Nature Medicine, Nature Biotechnology, Nature Chemical Biology, Nature Biomedical Engineering, Nature, and machine learning conferences including NeurIPS, ICML, and ICLR. He has received 6 best paper awards at NeurIPS/ICML workshops, ISMB, and ASHG, with cover article in Nature Biotechnology and Cell Patterns. His research has been featured in major media outlets such as Forbes, WIRED, and MIT Technology Review. He has also contributed to machine learning research at leading companies and institutions, including Genentech, GSK, Pfizer, IQVIA, Flatiron Health, Dana-Farber Cancer Institute, and Rockefeller University.

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/

Polygenic Prediction of Treatment Efficacy with Causal Transfer Learning
2026-03-23 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Jiacheng Miao, Stanford University

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

Therapeutic interventions often exhibit heterogeneous treatment effects (HTE) across individuals. A central goal of precision medicine is to enable personalized treatment recommendations based on patients’ measurable characteristics. However, detecting HTE remains challenging due to limited sample size in randomized controlled trials (RCTs), often-missing baseline information, and suboptimal statistical methods with limited power. In this talk, I will introduce a principled statistical framework named M-Learner to identify genetically-driven HTE. This approach leverages genetic variation involved in diverse biological pathways influencing drug response, integrates insights from two decades of complex trait genetics, and employs causal transfer learning applicable to both individual-level data and summary statistics. Applying M-Learner to multiple RCTs, we found low bone mineral density as a key determinant of secukinumab efficacy in ankylosing spondylitis, and identified smoker subpopulations adversely affected by a bronchodilator treatment. Our findings demonstrate the utility of genetic variation in HTE inference and make important advances toward the promise of precision medicine.

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Jiacheng Miao

Jiacheng Miao is a postdoctoral fellow at Stanford University working with Jonathan Pritchard and James Zou. He is building Agentic AI systems that augment and accelerate researchFind out more on Xhttps://x.com/Jiacheng_Miao

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: Dr 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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Dr Pan Liu

Dr 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

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data
2026-04-13 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Rafael dos Santos Peixoto, Johns Hopkins University

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

Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we apply CRAWDAD to various simulated and real SRO datasets across diverse tissues and SRO technologies. We further demonstrate how such multi-scale characterization, enabled by CRAWDAD, can be used to compare cell-type spatial relationships across multiple samples. Finally, we apply CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate that such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.

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Rafael dos Santos Peixoto

Rafael is a PhD candidate in Biomedical Engineering at Johns Hopkins University. Under the supervision of Jean Fan, he develops software to analyze spatial omics data. His first project was CRAWDAD, an R package to analyze cell-type spatial relationships. Now, he is investigating the molecular differences in acute kidney injury. Outside of the lab, he enjoys playing and watching sports. He is from Brazil, and hopes they will win this World Cup!Find out more on X and LinkedIn:https://x.com/rdsantospeixotohttps://www.linkedin.com/in/rafaeldossantospeixoto/

Integrative transcriptome-based drug repurposing in tuberculosis
2026-04-20 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Kewalin Samart, University of Colorado

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

Tuberculosis (TB) remains the leading cause of death from infectious disease, with rising antibiotic resistance highlighting the need for host-directed therapeutics (HDTs). Transcriptome-based connectivity mapping offers a promising strategy by identifying drugs that reverse disease gene expression signatures, but current approaches are limited by reliance on single datasets, platforms, or scoring methods.

Here, we present a unified computational framework that systematically integrates heterogeneous transcriptomic data and multiple connectivity methods for robust drug prioritization. Our approach combines 28 TB gene expression signatures spanning microarray and RNAseq platforms, diverse cell types, and infection conditions, and applies multi-method connectivity scoring to identify consistent disease-drug reversal signals. By aggregating signals across datasets and methods, the framework captures dominant TB signatures while mitigating platform and biological variability. Using this integrative strategy, we prioritized 64 FDA-approved drugs as candidate HDTs, including previously reported host-directed therapeutic candidates such as statins and tamoxifen. Downstream pathway and network analyses further revealed enrichment in TB-relevant mechanisms and identified key bridging genes (e.g., IL-8, CXCR2) as potential therapeutic targets.

This work establishes transcriptome-based connectivity mapping as a viable approach for systematic HDT discovery in bacterial infections and provides a robust computational framework applicable to other infectious diseases. Our findings offer immediate opportunities for experimental validation of prioritized drug candidates and mechanistic investigation of identified druggable targets in TB pathogenesis.

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Kewalin Samart

Kewalin Samart is a PhD candidate in Computational Bioscience at the University of Colorado Anschutz Medical Campus. She earned her B.Sc. in Computational Mathematics from Michigan State University. Her research focuses on developing and applying computational methods to uncover host response mechanisms and identify novel host-directed therapeutic strategies for infectious diseases.Find out more on X and LinkedIn:https://x.com/KewalinSamarthttps://www.linkedin.com/in/kewalinsamart

PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery
2026-05-04 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Dr Weize Xu, Stanford University

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

The convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However, existing biological agent systems, building upon single-agent architectures, are narrowly specialized or overly general, limiting applications to routine analyses. We introduce PantheonOS (https://PantheonOS.stanford.edu), an evolvable, privacy-preserving multi-agent framework designed to reconcile generality with domain specificity. Critically, PantheonOS enables agentic code evolution, allowing evolving state-of-the-art batch correction and our reinforcement-learning augmented gene panel selection algorithms to achieve super-human performance. PantheonOS drives biological discoveries across systems: uncovering asymmetric paracrine Cer1–Nodal inhibition in proximal–distal axis formation of novel early mouse embryo 3D data; integrating human fetal heart multi-omics with whole-heart data to reveal molecular programs underpin heart diseases; and adaptively selecting virtual cell models to predict cardiac regulatory and perturbation effects. Together, PantheonOS points towards a future where scientific discoveries are increasingly driven by self-evolving AI systems across biology and beyond.

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Dr Weize Xu

Dr. Weize Xu is a postdoctoral researcher in Dr. Xiaojie Qiu’s laboratory, where he focuses on advancing computational biology and genomics research. He earned his Ph.D. in Dr. Gang Cao’s lab, where he made significant contributions to the development of computational methods and pipelines for spatial transcriptomics (MiP-Seq) and single-cell Hi-C (sciDLO Hi-C). His work during this time centered on enhancing data analysis frameworks, providing more precise insights into complex biological systems.Find out more on X:https://x.com/Nanguage

Through the Unlabeled Lens of Spatial Multi-Omics
2026-06-01 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Dr Anthony A. Fung, Yale University

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

Sometimes it matters less what you look at, and more what you see. Common practices in clinical pathology often involve multiple histological stains on serial sections of tissue biopsy to obtain the highest diagnostic power, but this requires expertise, reagent costs, consumes tissue, risks deformation, and complicates co-registration, potentially missing rare microstructures. Now there is a major push for spatial multi-omics integration, but even adjacent tissue sections captured with different modalities decrease performance. Today’s seminar introduces a non-destructive label-free optical platform combining SRS, SHG, and TPF enables high-resolution molecular imaging to unravel the lipidomic, metabolic, and morphometric landscape of kidney disease, and how these data types can augment your modalities.

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Dr Anthony A. Fung

Dr Anthony A. Fung is a T32 postdoctoral fellow in Professor Rong Fan’s group at Yale University. He received his PhD in Bioengineering at University of California San Diego from Professor Lingyan Shi’s group. Anthony has received several awards in the quantitative spatial biology field and is a collaborating investigator in both HuBMAP and SenNet consortia. His current work centers on the development and application of spatial multi-omics technologies in aging and immune senescence.Find out more on LinkedIn:https://www.linkedin.com/in/anthony-fung/
