Sydney Precision Data Science Centre

Spatial and Functional Analysis of the Mammalian Brain
2025-08-04 13:00 - 14:00 (Australia/Sydney)
Statistical Bioinformatics SeminarDr Rongxin Fang, Stanford University

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

The mammalian brain is composed of an extraordinarily diverse cell types, yet how these cells are spatially organized in the human brain—and how this organization differs across species or in disease—remains poorly understood. In this talk, I will present two innovations in spatial transcriptomics aimed at addressing this gap. In the first part, I will introduce genome-scale MERFISH, a transcriptome imaging method capable of profiling over 4,000 genes in situ at single-cell resolution. Applying this approach to the human cerebral cortex, we identified more than 100 transcriptionally distinct cell populations and constructed a spatially resolved, molecularly defined cell atlas. Comparative analysis with mouse cortex revealed conserved laminar organization, but also species-specific differences—particularly in somatic interactions between neurons and non-neuronal cells—highlighting unique aspects of human brain organization. While MERFISH has primarily been applied to thin tissue sections (~10 µm), many biological questions require volumetric analysis of thicker tissue samples. In the second part of the talk, I will present 3D MERFISH, a method that combines MERFISH with confocal microscopy for optical sectioning and deep learning for enhanced image reconstruction. We applied this technique to mouse brain tissue up to 200 µm thick, achieving high detection sensitivity and spatial fidelity. Together, these genome-scale and volumetric transcriptome imaging methods expand the capabilities of spatial genomics, enabling unprecedented insight into brain organization, species evolution, and disease mechanisms.

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Dr Rongxin Fang

Dr Rongxin Fang is an Assistant Professor at Stanford University in the Departments of Neurosurgery and Genetics (by courtesy) and a member of the Wu Tsai Neuroscience Institute. Rongxin received his Ph.D. in Bioinformatics and Systems Biology in the Department of Molecular and Cellular Medicine at UC San Diego, where he was advised by Bing Ren (2015-2019). Rongxin has also received multiple fellowships and awards, including the HHMI/Damon Runyon Postdoctoral Fellowship, Rising Star in Health and Engineering – Johns Hopkins and Columbia University, Next Generation Leader – Allen Institute, Damon Runyon-Dale F. Frey Award for Breakthrough Scientists.

TenK10K multiome initiative: Genetic regulation of cell-type–specific chromatin accessibility shapes immune function and disease risk
2025-08-11 13:00 - 14:00
Statistical Bioinformatics SeminarDr Angli Xue, Garvan Institute of Medical Research

This was an online event held via Zoom.

Understanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex diseases. However, limited large-scale single-cell multi-omics data have constrained our understanding of the regulatory pathways that link variants to gene expression. Here, we present the TenK10K multiome initiative as part of the TenK10K phase 1 projects: chromatin accessibility profiles from 3.5 million peripheral blood mononuclear cells (PBMCs) across 1,013 donors, generated using single-cell ATAC-seq and multiome (RNA+ATAC) sequencing, with matched whole-genome sequencing. We characterised ~441,000 chromatin peaks across 28 immune cell types and mapped ~243,000 chromatin accessibility QTLs (caQTLs), 60% of which are cell-type-specific. Integration with TenK10K scRNA-seq (5.4 million PBMCs) identified 31,688 candidate cis-regulatory elements colocalised with eQTLs; over half (52.5%) show evidence of causal effects mediated via chromatin accessibility. Combining caQTLs with GWAS loci for 17 diseases and 44 blood traits uncovered 10–41% more colocalised signals compared to eQTLs alone. For example, incorporating caQTLs increased the number of candidate inflammatory bowel disease (IBD) genes in CD8 effector memory T cells from 39 to 55. We demonstrate cell-type-specific mechanisms, such as a regulatory effect on IRGM acting through altered chromatin accessibility in CD8 effector memory T cells but not in naïve cells. Using a graphical neural network, we link peaks to genes in unpaired multiome data with up to 80% higher accuracy than with paired data lacking QTLs, improving gene regulatory network inference by identifying 128 additional TF–target pairs (a 22% increase). These findings provide an unprecedented single-cell map of chromatin accessibility and genetic variation in human circulating immune cells, establishing a powerful resource for dissecting cell-type-specific regulation and advancing our understanding of genetic risk for complex diseases.

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Dr Angli Xue

Dr Angli Xue is an NHMRC Investigator Fellow (EL1) and a postdoctoral researcher with Prof Joseph Powell at the Garvan Institute of Medical Research. He currently leads the multiome stream of the TenK10K project — a population cohort study aiming to map 50 million human cells from 10,000 individuals. His research focuses on leveraging large-scale multi-omics data to uncover cell type–specific genetic regulatory mechanisms and identify novel drug targets. Dr Xue earned his BSc from Zhejiang University and completed a PhD in Statistical Genetics at The University of Queensland under the supervision of Prof Jian Yang. He is the recipient of an NHMRC Investigator Grant (2025–2029) and a Ramaciotti Health Investment Grant as Chief Investigator.

The lazy way of pathological images analysis in the era of large models
2025-08-18 13:00 - 14:00
Statistical Bioinformatics SeminarDr Yimin Zheng, CeMM Research Center for Molecular Medicine

This was an online event held via Zoom.

Histopathological slides remain the gold standard for disease diagnosis and the study of tissue architecture and pathology, offering an exceptionally rich yet often underutilized source of biomedical information. Unlike single-cell datasets, pathology slides are readily available in clinical workflows and comparatively inexpensive to generate.

Recent advances in foundation models have transformed our ability to extract meaningful insights from image data, leading to a surge of high-impact studies leveraging pathology images for survival prediction, subtyping, spatial integration, and more. However, for many bioinformaticians and systems biologists, the field of image analysis still feels inaccessible because of steep technical barriers: diverse file formats, fragmented tools, and a lack of intuitive frameworks for exploration and analysis.

Is there a way to analyze pathology images as intuitively as we analyze single-cell data? The answer is yes!

In this talk, I will introduce LazySlide, an open-source platform for pathology image analysis specifically designed for users scientists already comfortable with tools like Scanpy and AnnData. LazySlide supports multiple image formats and enables interaction with large models. This talk will demonstrate how LazySlide empowers scientists to “analyze images with text” and incorporate rich histopathological information into multi-omics workflows, without needing to become image processing experts. Whether you’re studying cancer progression, tissue aging, or immune landscapes, LazySlide unlocks a new dimension of spatial and morphological context at scale.

Get started with LazySlide in no time: https://github.com/rendeirolab/LazySlide

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Dr Yimin Zheng

Dr Yimin Zheng is Postdoctoral Fellow at CeMM Research Center for Molecular Medicine at Vienna, Austria. He comes from a spatial biology background. His current research focuses on understanding cancer metastasis and aging from a multimodel pathological imaging perspective. He is also an open source contributor to multiple scientific software projects and a big-fan on visualization who has developed the composable visualization package of Marsilea.

The secret sauce - building insights from integrated atlas of human biology
2025-08-25 13:00 - 14:00 | Mackenzie Room, Level 6, Charles Perkins Centre, University of Sydney, Johns Hopkins Drive, University of Sydney, Sydney, 2006, Australia
Judith and David Coffey SeminarProf Christine Wells, University of Melbourne

This event was held in-person and online.

The human cell atlas is a global consortium seeking to build a detailed map of cell types through developmental time, health and disease states. Molecular snapshots of individual cells can be taken at many measurement levels, and the expectation is that integrating multiple modalities will lead to new discoveries about human cell development and behaviour. Data integration at this scale requires two main considerations – the first is statistical models to remove experimental artefacts and harmonise informative signals ; the second is curation of the metadata to identify possible technical, experimental or even biological confounders. We often pay a lot of attention to the first challenge, at the expense of minimizing or ignoring the second, simply because data curation is difficult, and often requires biological expertise that sits outside of our own groups.

In this talk, I’ll introduce generative AI driven tools developed by my own team for stem cell curation, as well as provide a preview of the community-driven curation platforms being lead out of the Human Cell Atlas consortium and provide some examples of how these tools are supporting high quality data integration and the creation of new cell atlases.

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Prof Christine Wells

Professor Christine Wells is chair of stem cell systems at the University of Melbourne. She uses computational models to understand human stem cell behaviour, and pluripotent stem cell models to test these computational predictions. Christine graduated with a PhD from UQ in 2004, and established her first laboratory at Griffith University in 2005. Over the past 20 years, her team has built several human cell atlases describing immune cell subsets, then uses these atlases to benchmark lab grown immune cells, and have invented new protocols to make specialist immune cells in the laboratory. Christine has authored over 150 papers, collectively cited >30,000 times. She is a member of several international consortia including the Functional Annotation of the Mammalian genome (FANTOM), the Human Cell Atlas (HCA) and the equity working group for the International Society for Stem Cell Research. Christine is the architect of the Stemformatics data collaboration platform and academic lead of the Australian stem cell registry.

A Systematic Comparison of Single-Cell Perturbation Response Prediction Models
2025-09-01 13:00 - 14:00
Statistical Bioinformatics SeminarDr Yue You, Guangzhou National Laboratory

This was an online event held via Zoom.

Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale benchmark of 12 representative methods and 3 baselines across 25 datasets spanning diverse perturbation modalities and species, including two new primary immune-cell drug-response resources. We evaluated three core tasks—generalization to unseen single-gene perturbations, prediction of combinatorial interactions, and transfer across cell types—using 24 metrics covering expression-level accuracy, relative changes, differential expression recovery, and distributional similarity.

Across tasks, performance depended strongly on perturbation effect size and evaluation perspective: expression-level agreement was highest for small-effect perturbations resembling controls, whereas delta- and DE-based metrics improved with larger effects, providing clearer signals. Models shared a conservative bias, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2, while no method consistently generalized across cell types in Task 3, where dataset heterogeneity dominated outcomes.

This benchmark establishes current methodological limits, clarifies when metrics diverge, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses.

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Dr Yue You

Dr Yue You is a postdoctoral researcher at the Guangzhou National Laboratory. Her research centers on developing single-cell multi-omics algorithms and spatial-omics data analysis to decode cellular dynamics under genetic and pharmacological perturbations.

Large Models for Single-Cell Omics and Drug Discovery: Data, Pretraining, and Closed-Loop Environment
2025-09-08 13:00 - 14:00
Statistical Bioinformatics SeminarDr Haotian Cui, University of Toronto

This was an online event held via Zoom.

This presentation will discuss recent advances in foundation models for single-cell omics and therapeutic discovery, focusing on the development of scGPT, a generative transformer model trained on over 33 million single-cell profiles. It will cover key design principles—such as generative pretraining and multi-task alignment—that enable broad applications including cell type annotation, perturbation response prediction, and reference mapping. The talk will also highlight emerging multimodal extensions such as scGPT-spatial and MethylGPT, and emphasize the importance of perturbational training for modeling cellular dynamics. Finally, it will introduce LUMI-lab, a closed-loop self-driving platform developed by Dr. Cui and collaborators, which integrates model-guided design, synthesis, and validation to accelerate mRNA delivery—demonstrating a scalable framework for virtual cell modeling and active learning in biology.

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Dr Haotian Cui

Dr. Haotian Cui is a researcher specializing in machine learning, genomics, and drug discovery. He recently completed his Ph.D. in Computer Science at the University of Toronto, advised by Prof. Bo Wang. His research focuses on developing large-scale self-supervised and generative foundation models for single-cell omics and molecular biology, integrating AI with experimental pipelines to accelerate therapeutic discovery. He is the lead author of scGPT—one of the first generative foundation models for single-cell multi-omics. He also led the development of LUMI-lab, an autonomous AI-driven platform for mRNA therapeutics. He has published numerous papers in leading journals and conferences, including Nature, Nature Methods, Nature Communications, ACL, EMNLP, ICML.

Bifidobacteria support optimal infant vaccine responses
2025-09-15 13:00 - 14:00
Statistical Bioinformatics SeminarDr Feargal J. Ryan, Flinders University

This was an online event held via Zoom.

Accumulating evidence indicates that antibiotic exposure may lead to impaired vaccine responses; however, the mechanisms underlying this association remain poorly understood. Here we prospectively followed 191 healthy, vaginally born, term infants from birth to 15 months, using a systems vaccinology approach to assess the effects of antibiotic exposure on immune responses to vaccination. Exposure to direct neonatal but not intrapartum antibiotics was associated with significantly lower antibody titres against various polysaccharides in the 13-valent pneumococcal conjugate vaccine and the Haemophilus influenzae type b polyribosylribitol phosphate and diphtheria toxoid antigens in the combined 6-in-1 Infanrix Hexa vaccine at 7 months of age. Blood from infants exposed to neonatal antibiotics had an inflammatory transcriptional profile before vaccination; in addition, faecal metagenomics showed reduced abundance of Bifidobacterium species in these infants at the time of vaccination, which was correlated with reduced vaccine antibody titres 6 months later. In preclinical models, responses to the 13-valent pneumococcal conjugate vaccine were strongly dependent on an intact microbiota but could be restored in germ-free mice by administering a consortium of Bifidobacterium species or a probiotic already widely used in neonatal units. Our data suggest that microbiota-targeted interventions could mitigate the detrimental effects of early-life antibiotics on vaccine immunogenicity.

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Dr Feargal J. Ryan

Dr Feargal J. Ryan is an NHMRC Investigator and head of the Computational Multi-Omics Group at Flinders University. He specializes in systems biology, bioinformatics and the use of multi-omics data to study molecular mechanisms of health and disease in humans. Dr Ryan has co-authored over 45 peer reviewed papers including in top journals such as Nature and Science, which have spanned the human microbiome, idiopathic diseases, infection and cancer. He is also an advocate for supporting bioinformatics research in Australia and last year was elected as Vice-President of the national Australian Bioinformatics and Computational Biology Society (ABACBS).

Cell Simulation as Cell Segmentation
2025-09-22 13:00 - 14:00
Statistical Bioinformatics SeminarDr Daniel Jones, Fred Hutchinson Cancer Institute

This was an online event held via Zoom.

Spatial transcriptomics has grown rapidly in scale and adoption, but the quality and interpretability of the data is marred by the limited ability of prior cell segmentation methods to accurately assign transcripts to cells. Segmentation error causes systematic misidentification of cell types and profoundly confounds many spatial analyses. To help address this issue, we developed a probabilistic segmentation algorithm, Proseg, which uses the spatial distribution of transcripts to determine plausibly boundaries, dramatically reducing the degree of spurious coexpression downstream false positive results. Recent updates to the method adds support for Visium HD, handling both imaging- and barcode-based transcriptomic data with the same unified segmentation framework.

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Dr Daniel Jones

Daniel Jones is a staff scientist at the Fred Hutchinson Cancer Institute in the lab of Evan Newell since 2020 after receiving his PhD in Computer Science and Engineering at the University of Washington. His work focuses on probabilistic modeling and inference in spatial transcriptomics.

Open-ST: High-resolution spatial transcriptomics in 2D and 3D
2025-09-29 13:00 - 14:00
Statistical Bioinformatics SeminarDaniel León-Periñán, Berlin Institute for Medical Systems BiologyDr Elena Splendiani, Sapienza University of Rome

This was an online event held via Zoom.

Spatial transcriptomics (ST) has significantly advanced our understanding of the molecular mechanisms involved in tissue development, homeostasis, and disease. However, there is a need for easy-to-use, high-resolution and cost-efficient methods that can be scaled up for the analysis of tissues in 3D. To address these challenges, we introduce Open-ST, a high-resolution, sequencing-based platform, designed for the analysis of tissue molecular organization in both 2D and 3D. This experimental and computational resource is open-source, modular and cost-effective, making it accessible to a broad range of researchers and facilitates the adaptation of new implementations.

Open-ST has proven effective in various contexts. In mouse brain tissue, it captured transcripts at subcellular resolution and successfully reconstructed cell types. In a primary head-and-neck tumor and patient-matched healthy and metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations, findings that were corroborated by imaging-based ST. Notably, distinct cellular states were organized around cell-cell communication hotspots in the primary tumor. These transcriptomic states were maintained in the metastasis, despite the spatial organization being disrupted. Reconstructing the metastatic lymph node into a “3D virtual tissue block” from serial tissue sections, identified spatially contiguous structures that were not discernible in 2D. These included potential biomarkers located at the 3D tumor/lymph node boundary.

Currently, we are extending Open-ST to increase its applicability to a wider range of research questions, from small RNAs to long-reads, as well as extending its application to formalin-fixed paraffin-embedded tissues. Furthermore, integrating immunofluorescence staining in the workflow allows a multiomic perspective on the tissue architecture. Given its accessibility and versatility, Open-ST can be adopted and customized by a diverse range of users, enabling its application to increasingly specialized studies.

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Daniel León-Periñán

Dani is a computational biologist pursuing his PhD in the Rajewsky lab at the Berlin Institute for Medical Systems Biology (BIMSB-MDC). His research focuses on understanding how gene expression patterns, in space and time, can predict disease progression, by developing methods that generate, analyze and integrate large-scale data. With a background in Biotechnology and Computer Science, he works at the intersection of spatial biology, data visualization, and machine learning, with the aim to make biological data accessible and searchable.

Dr Elena Splendiani

Elena is a postdoctoral research fellow at the Department of Experimental Medicine, Sapienza University of Rome. She received her PhD in Molecular Medicine from Sapienza University in 2023 and her Master’s degree in Genetics and Molecular Biology in 2019 from the same institution. Her research focuses on solid tumors, with a particular interest in biomarker discovery and RNA-level molecular characterization, including both coding and non-coding RNAs. She co-developed a high-spatial-resolution technology, Open-ST, which she is currently applying to characterize tumors and study their microenvironments.

Using genomics-informed agent-based models to understand neoplastic phenotype transitions in the human PDAC microenvironment
2025-10-13 13:00 - 14:00
Statistical Bioinformatics SeminarDr Jeanette Johnson, University of Maryland

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

We present a novel integration of data-driven single-cell analysis with mechanistic mathematical modeling to predict the impact of cancer-associated fibroblasts (CAF) on pancreatic ductal adenocarcinoma (PDAC) invasion. Bioinformatic analyses of high-throughput single-cell and spatial molecular assays give us access to human tumor gene expression at the time of measurement, enabling direct characterization of the human tumor microenvironment (TME), complementing our understanding of cell behavior gained from preclinical models. Notably, applying our Bayesian non-negative matrix factorization algorithm CoGAPS to single-cell RNA-seq data from PDAC allowed us to associate a gene program of concurrent epithelial-to-mesenchymal transition (EMT) and inflammation in epithelial cells with the presence of CAFs, validated through organoid co-culture experiments. We hypothesize that this gene program represents a mechanism by which CAFs promote invasive behavior in neoplastic cells over time. Testing this hypothesis requires inference of temporal changes, which is not possible with single-timepoint data collection in genomics data. In contrast to data-driven bioinformatics, mechanistic mathematical modeling can capture processes that happen over time and make predictions about a system. Agent-based models (ABMs) are one form of such mechanistic mathematical models that are well suited for temporal modeling of cellular phenotypes. We developed new software for ABMs that abstract cells into software agents with individual states and their own rules of behavior from single-cell and spatial molecular assays for temporal inference. We apply this technique to understand how CAFs in the PDAC microenvironment support the series of phenotype transitions comprising tumor progression and invasion, by encoding the CAF-induced ability of tumor cells to migrate and a cessation of proliferation in tumor cells, induced by CAF-secreted, non-diffusable substrate as inferred from our genomics analysis. While this provides a virtual framework to test the impact of varying CAF density on tumor cell invasion, it initially lacked integration with the spatial architecture of human tumors. We then apply the ABM to human PDAC spatial transcriptomics data to forecast tumor behavior over time in a spatially-informed setting, varying the hypothesized mechanisms. This revealed that a substrate-driven mechanism results in a broad mesenchymal-like zone surrounding epithelial-like outgrowths, while a cell contact-mediated mechanism results in a single-cell-wide boundary surrounding more significant outgrowth. When we compared these to real tumor compositions, this favors the explanation that secreted CAF substrate drives EMT in PDAC neoplastic cells, which we then validate in organoids with CAF conditioned media. Our work demonstrates how integrating ABMs with genomic and spatial data enables testing of mechanistic hypotheses about tumor-stroma interactions and provides deeper insights into PDAC microenvironments, potentially informing strategies to intercept disease progression.

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Dr Jeanette Johnson

I am currently a postdoctoral fellow at the University of Maryland School of Medicine, after completing my PhD in Immunology at Johns Hopkins this past May. My genomics obsession started as an undergraduate student at the University of British Columbia in computer science and immunology when I got to write processing scripts for some single-cell PBMC data. I will be presenting some of my PhD work which I did in Dr. Elana Fertig’s lab, looking at ways to build agent-based computational models systematically from genomics data, which I developed in the context of the pancreatic tumor microenvironment. Living in Baltimore, Maryland with my two cats and partner, my favorite hobby is convincing people to go eat seafood with me. I also love to do my nails and am actively searching for the maximally dramatic set that still lets me type and pipette.

High-resolution Characterization of Age-specific Changes in HPV-negative HNSCC through Building a scRNA-Sequencing Atlas
2025-10-20 13:00 - 14:00
Statistical Bioinformatics SeminarLina Kroehling, Boston University

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

Age is strongly associated with both the incidence and mortality risk of head and neck cancer. While cancer and aging share many of their defining “hallmarks”, including chronic inflammation, increased genomic instability, and increased senescence, more research is needed to elucidate the specific mechanisms contributing to cancer aggressiveness in older patients.

To this end, we have assembled a hiqh-quality human single-cell RNA-sequencing HNSCC atlas profiling more than 290,000 cells across more than 70 patients, with ages ranging between 18 and 90, which provides a unique resource to investigate age-associated changes in the disease’s heterogeneity.

To create the atlas, we integrated seven publicly available single-cell RNAseq datasets from 73 HPV-negative patients. Cells were clustered, classified, and characterized by gene set enrichment analysis, both in the epithelial cell compartment and in the tumor microenvironment (TME). Differential expression and cell type proportion analyses were performed to identify genes and cell type compositional changes associated with age. Cell cell communication analysis was performed to identify interacting cell types and modeled to identify specific ligands and receptors changing with age.

We identified distinct age-related changes in cell type composition, including vascular endothelial cells increasing with age, and several tumor clusters with distinct functions also changing with age. Further analyses are ongoing, and we plan to functionally validate the hypotheses generated, specifically the presence of differentially abundant cell populations, and age-specific ligand-receptor signaling events that contribute to tumor growth.

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Lina Kroehling

Lina Kroehling is a bioinformatics PhD candidate in the Monti Lab at Boston University where she utilizes multi-omics data to study Head and Neck Cancer. She has a B.S. and M.S. in biochemistry from Clark University in Worcester MA, and worked as a bioinformatician in academic immunology labs before starting her PhD.

Harnessing the human microbiome for therapeutic purposes
2025-10-27 13:00 - 14:00
Statistical Bioinformatics SeminarDr Shanlin Ke, The Ohio State University

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

The human microbiome plays a critical role in health and disease and represents a promising source for novel therapeutic strategies. In this talk, I will first provide an overview of how microbiomes are currently leveraged for therapeutic purposes. I will then introduce a computational approach we have developed to uncover complex host–microbiota interactions. Using our recent PTSD–microbiome study as an example, I will demonstrate how this approach can reveal host–microbiota interactions and be validated through animal models. Next, I will present our AI-based framework for discovering antimicrobial peptides (AMPs) from the urinary microbiome, highlighting its potential to combat antimicrobial resistance. I will conclude by discussing an idea: rejuvenating the human gut microbiome to preserve the “healthy” microbiome for future therapeutic use.

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Dr Shanlin Ke

Dr Shanlin Ke joined The Ohio State University as an Assistant Professor in May 2025 in the Division of Gastroenterology, Hepatology, and Nutrition within the Department of Internal Medicine. Dr Ke is a biologist with expertise in bioinformatics, microbiome, machine learning, multi-omics, and wet-lab techniques. He received his PhD in Animal Genetics from Jiangxi Agricultural University in 2020. In 2018, he joined Dr Yang-Yu Liu’s lab at the Channing Division of Network Medicine, Harvard Medical School as a visiting student. He continued his microbiome research as a postdoctoral fellow in Dr Liu’s lab beginning in 2021, where he developed and applied computational approaches to study host–microbiota interactions in chronic conditions (e.g., inflammatory bowel disease and post-traumatic stress disorder) and infectious diseases (e.g., Clostridioides difficile infection and urinary tract infection). In parallel, he conducted in vitro and in vivo experiments to validate computational findings and investigate the mechanisms underlying host-microbiota interactions in human diseases.

At OSU, Dr Ke’s lab focuses on developing innovative methodologies and leveraging bioinformatics tools, metagenomic sequencing, machine learning, and wet-lab techniques to investigate the role of the human microbiome in pancreatic diseases and to develop microbiome-based therapeutics.

Australian Data Science Network Conference 2025
2025-11-10 08:00 - 2025-11-12 05:00
Sydney Precision Data Science Centre is delighted to host the 4th Australian Data Science Network (ADSN) conference. The ADSN conference aims to connect Australia’s top experts in data science, fostering collaboration, expanding opportunities, and showcasing our collective capabilities.

Australian Data Science Network Conference 2025 Link

DETAILS

November 10-12

ORGANISERS

Charles Perkins Centre

University of Sydney

ORGANISERS

Australian Data Science Network

University of Sydney

ELLA: modelling subcellular spatial variation of gene expression
2026-02-23 13:00 - 14:00
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: 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

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 is 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
