Sydney Precision Data Science Centre

Bifidobacteria support optimal infant vaccine responses
2025-09-15 13:00 - 14:00 (Australia/Sydney)
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 was 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 was 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 Pennsylvani

This was 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 was 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: Dr Jiacheng Miao, Stanford University

This was 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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Dr Jiacheng Miao

Dr 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 was 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 was an online event held via Zoom.

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

Coupling Single-cell Genome & Epigenome to Study Functional Consequence of Somatic SVs
2026-05-11 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Dr Hyobin Jeong, Yonsei University

This was an online event held via Zoom.

Somatic structural variants are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We proposed a computational method, scNOVA, which utilizes Strand-seq to perform haplotype-aware integration of structural variant discovery and molecular phenotyping in single cells, by using nucleosome occupancy to infer gene expression as a read-out. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of structural variants on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T-cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. More recently, scNOVA to complex karyotype acute myeloid leukemia (AML) revealed dynamic clonal evolution and targetable phenotypes. Not only cancer system, we can apply this approach to study functional effect of somatic SVs in clonal hematopoiesis and aging. Also, we are now extending scNOVA to develop scalable and broadly applicable bioinformatics methods which can link SVs to their functional effects, to enable systematic single-cell multiomic studies of structural variation in heterogeneous cell populations.

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Dr Hyobin Jeong

Dr Hyobin Jeong is an assistant professor at Yonsei University in the Department of Systems Biology. She received her PhD in Interdisciplinary Bioscience and Biotechnology from Pohang University of Science and Technology (POSTECH). She has completed postdoctoral fellowships at the European Molecular Biology Laboratory (EMBL) and the Institute of Molecular Biology (IMB) in Germany, and the Institute of Basic Science in Korea. Before her current role, she was a Research Professor at the Hanyang Institute of Bioscience and Biotechnology.

Genetic regulation of human skeletal muscle: from bulk QTLs to single-nucleus resolution
2026-05-18 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Wang Wenjing, National University of Singapore

This was an online event held via Zoom.

Although lifestyle-induced weight loss reduces type 2 diabetes risk, how genetic variation shapes gene expression and splicing responses to weight loss remains poorly understood, particularly in Asian populations. In the first part of this talk, I will present our recently published work (Cell Genomics, 2025) in which we profiled skeletal muscle transcriptomes from 54 overweight/obese Asian individuals before and after a 16-week lifestyle intervention in the Singapore Adult Metabolism Study (SAMS). The intervention resulted in ~10% weight loss and ~30% improvement in insulin-stimulated glucose uptake. We identified 505 differentially expressed genes enriched in mitochondrial function and insulin sensitivity pathways, mapped cis-eQTLs and cis-sQTLs that are shared or condition-specific, and integrated these regulatory variants with GWAS signals for metabolic traits to pinpoint candidate causal genes and mechanisms. In the second part, I will introduce our ongoing effort to build a comprehensive single-nucleus and spatial transcriptomic atlas of human skeletal muscle. Using snRNA-seq from over 300 biopsies spanning lean and obese individuals across three Asian ancestries, we have profiled more than one million nuclei and annotated over 20 cell types capturing substantial cellular heterogeneity across ancestry, adiposity, and metabolic states. We are now extending this resource with spatial transcriptomics (MERFISH and Xenium) on paired pre- and post-intervention samples to spatially resolve cell-type-specific regulatory programs and microenvironment remodeling during weight loss. Together, these efforts establish the first longitudinal, ancestry-diverse single-nucleus and spatial regulatory reference for Asian human skeletal muscle.

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Wenjing Wang

Wenjing Wang is a third-year PhD candidate in the lab of Prof. Liu Boxiang at the National University of Singapore, working at the intersection of functional genomics and metabolic disease. Her work combines bulk and single-nucleus RNA sequencing with QTL mapping to understand how genetic variation and lifestyle interventions reshape gene regulation across cell types. Her recent study on gene expression and splicing responses to exercise- and diet-induced weight loss was published in Cell Genomics (2025). She is currently building a large-scale single-cell and spatial atlas of human skeletal muscle across diverse Asian populations.

Find out more on LinkedIn: https://www.linkedin.com/in/wenjing-wang-40a67b18b/

Generalized cell phenotyping for spatial proteomics with language-informed vision models
2026-05-25 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Xuefei (Julie) Wang, California Institute of Technology

This was an online event held via Zoom.

We present DeepCell Types, a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels collected across different platforms. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this model’s semantic understanding of the underlying marker panel enables it to learn from and adapt to heterogeneous datasets. Leveraging a curated, diverse dataset named Expanded TissueNet with cell type labels spanning the literature and the NIH Human BioMolecular Atlas Program (HuBMAP) consortium, our model demonstrates robust performance across various cell types, tissues, and imaging modalities. Comprehensive benchmarking shows superior accuracy and generalizability of our method compared to existing methods. This work significantly advances automated spatial proteomics analysis, offering a generalizable and scalable solution for cell phenotyping that meets the demands of multiplexed imaging data.

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Xuefei (Julie) Wang

Xuefei (Julie) Wang is a PhD student at Caltech co-advised by David Van Valen and Yisong Yue. Her research focuses on the fundamental principles of foundation models and agentic systems, with a particular emphasis on spatial proteomics and transcriptomics. Julie’s work addresses the challenges of large-scale biological data through innovative modeling, including the development of language-informed vision models for generalized cell phenotyping. Her goal is to build knowledge-centric agents that accelerate discovery by compounding insights from foundation tools and literature into a persistent knowledge base that grows more capable with use. In addition to her work at Caltech, she has contributed to scientific AI efforts as a Student Researcher at Google Research with Michael P. Brenner.Connect with Xuefei:LinkedIn: xuefei-wangX: xuefei_whttps://xuefei-wang.github.io/

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 was an online event held via Zoom.

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/

Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics
2026-06-15 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Long Yuan, Johns Hopkins University

This was an online event held via Zoom.

In imaging-based spatial transcriptomics, transcript-to-cell assignment shapes downstream biological interpretation, including cell typing, ligand–receptor inference, and niche characterization. However, two-dimensional segmentation of volumetric tissue often yields mixed cellular profiles, while cells without detected nuclei may be missed entirely, affecting downstream analyses. We present TRACER, a framework that refines cellular representations in imaging-based spatial transcriptomics by leveraging gene–gene coherence and spatial co-localization of transcripts observed directly in the data, without requiring external annotations or reference atlases. TRACER resolves mixed cellular profiles and reconstructs partial cells whose nuclei are not detected, enabling more complete representation of cells within tissue sections. We also introduce coherence-based metrics that quantify transcriptional purity and conflict, enabling platform-agnostic benchmarking of segmentation quality. Across diverse platforms, tissues, and segmentation methodologies, TRACER consistently improves the coherence of cellular profiles and the quality of downstream analyses.

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Long Yuan

Long Yuan is a PhD candidate in Immunology and an M.S.E. candidate in Computer Science at Johns Hopkins University. His research focuses on developing scalable machine learning and statistical methods for spatial and single-cell omics, with applications in cancer biology and immune-metabolic diseases. His work spans spatial multi-omics, graph-based learning, and multimodal data integration. As a member of the Break Through Cancer GBM and Data Science TeamLab, he develops computational approaches for integrating and analyzing large-scale spatial omics datasets.Find out more on LinkedIn and X:https://www.linkedin.com/in/long-yuan-3a8b953aa/@Long_et_al

Statistical Brain Network Analysis: Recent Developments and Future Directions
2026-06-22 13:00 - 14:00
Judith and David Coffey SeminarSpeaker: Prof Sean L. Simpson, Wake Forest University

This was a hybrid event. In-person in the Mackenzie Seminar Room, Level 6, Charles Perkins Centre and online via Zoom.

The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, many statistical challenges remain to be able to fully realize the promise of this field. Here we touch on a few of these challenges, briefly survey three complementary statistical frameworks that we have developed to attempt to address a subset of these needs—a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework—and discuss potential future avenues of research.

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Prof Sean L. Simpson

Prof Sean L. Simpson is a biostatistician in the Department of Biostatistics and Data Science, with joint appointments in Biomedical Engineering and Neuroscience, and an Affiliate appointment with the Maya Angelou Center for Healthy Communities (MARCH) at Wake Forest University School of Medicine. His main research focus has been on the development of novel fusions of statistical tools with network science methods for the analysis of whole-brain network data. Studying the brain as a whole and statistically accounting for the inherent complexity in the way various regions of the brain interact will engender a more biologically meaningful approach to understanding the root causes of a number of brain diseases and disorders.

Microcredential: Data Analysis for Precision Health
2026-07-28 14:00 - 2026-07-29 17:00 | Room 4 & 5, Level 16 – The University of Sydney Business School – CBD Campus, Room 4 & 5, Level 16 - The University of Sydney Business School - CBD Campus, Sydney, NSW, 2000, Australia
As we enter the data revolution, the scale and accessibility of health and medical data has reached unprecedented levels creating a growing need for expertise in extracting insights from this data.

This course will provide participants with essential statistical skills to analyse and interpret health and medical data. Key topics include linear models, mixed effect models, logistic regression and survival analysis.

Real health and medical data will be utilised to explore common challenges, practical workarounds, and translate data into actionable insights.

By the end of this course, you will be able to:

formulate and interpret appropriate linear models to describe the relationships between multiple factors

train and evaluate logistic regression models for binary data

understand and apply linear mixed effect models for data with repeated measures

visualise survival data with Kaplan-Meier curves and perform inference with Cox proportional hazards models.

Aims

This course will give participants the necessary understanding and skills to perform statistical analyses on health and medical data.

Participants will gain experience in working with real data and develop critical thinking skills to address common challenges.

Participants will learn to communicate their data and findings through graphical and statistical summaries.

Content

The course covers four main topics:

Linear models: Fit, refine, interpret and visualise regression models. We will discuss making predictions, evaluating model fit and feature selection.

Logistic regression: Interpret odds ratios to evaluate risk factors, assess model performance when working with binary health outcomes.

Mixed effect models: Analyse repeated measures and hierarchical data to understand individual and group-level patterns in health and medical contexts.

Survival analysis: Model time-to-event data using Kaplan-Meier curves and Cox proportional hazards models, and assess model accuracy with metrics like the C-index.

Interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources
2026-08-03 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Dr Jia Zhao, Yale University

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

Recent advances in spatial transcriptomics have generated an expanding collection of heterogeneous datasets, offering unprecedented opportunities to investigate tissue organizations and functions. However, effective interpretation and integration of data originating from diverse sources and conditions remain a major challenge. In this talk, we will present INSPIRE, a deep-learning method for interpretable, integrative analysis of multiple spatial transcriptomic datasets. INSPIRE adopts an adversarial learning strategy with graph neural networks to achieve spatially-informed and adaptive data integration. By incorporating non-negative matrix factorization, INSPIRE identifies interpretable spatial factors and associated gene programs that characterize tissue architecture, cell-type organization, and biological processes. Across a broad range of applications, INSPIRE demonstrates superior performance in resolving fine-grained biological signals, integrating complementary strengths across technologies, capturing condition-specific variation, uncovering tumor microenvironment heterogeneity, elucidating developmental dynamics, and facilitating three-dimensional tissue reconstruction. INSPIRE also scales to extremely large datasets, as demonstrated by applications to Xenium-profiled human breast cancer and Stereo-seq mouse organogenesis datasets.

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Dr Jia Zhao

Dr Jia Zhao received her PhD in Mathematics from the Hong Kong University of Science and Technology under the supervision of Prof. Can Yang. She completed her postdoctoral training in the Department of Biostatistics at Yale University, mentored by Prof Hongyu Zhao and Prof Rui Chang. She will soon join the School of Mathematical Sciences at Shanghai Jiao Tong University as a faculty member. Her research focuses on developing computational methods to address analytical challenges arising from large-scale biomedical data. She has led the development of methods for integrative single-cell analysis, interpretable modeling of spatial omics data, and robust causal inference among complex traits. Her work has been published in leading journals, including Nature Genetics, Nature Machine Intelligence, Nature Computational Science, and PNAS.Find out more on X:@JiaZhao_stat

SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics
2026-08-10 13:00 - 14:00
Statistical Bioinformatics SeminarSpeaker: Dr Xiting Yan, Yale University

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

Spatially resolved transcriptomic (SRT) data requires spatial domain identification to enable tissue microenvironment-specific downstream analyses. Here we present SPEAK (Spatial Prompting with Expert-Aligned Knowledge), a large language model (LLM)-based method to identify spatial domains from SRT data by taking advantage of the prior knowledge from both LLM and human experts. SPEAK constructs a spatial context prompt for each cell/spot based on cell types and marker genes of its neighboring cells, enabling zero-shot inference, expert-guided fine-tuning, and prototype updating through two-stage prompting. Applications to STARmap, Visium, MERFISH and Xenium datasets showed advantages of SPEAK over existing spatial domain identification methods in domain prediction accuracy, robustness to limited prior knowledge, biological interpretability, and capacity for efficient expert-guided fine-tuning with generalizability to other tissue sections.

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Dr Xiting Yan

Dr Yan is an Associate Professor from the Section of Pulmonary, Critical Care and Sleep Medicine at Yale University School of Medicine, with a secondary appointment at the Department of Biostatistics at Yale University School of Public Health. She is the Director of Data Analysis and Bioinformatics Hub at the Cent for Precision Pulmonary Medicine (P2MED). Dr Yan is a world known computational biologist, bioinformatician and biostatistician, with extensive research experiences in large scale multi-omic data analyses at both bulk and single-cell resolution. Her current research interest focus on two parts: (1) developing novel statistical and computational models to analyze large scale multi-omics and drug perturbation data to better understand disease pathogenesis and facilitate precision medicine development, and (2) understanding the heterogeneity, pathogenesis and progression of pulmonary diseases, such as asthma, idiopathic pulmonary fibrosis (IPF), sarcoidosis, chronic obstructive pulmonary disease (COPD), pediatric cystic fibrosis and so on, by tailoring statistical and computational methods based on existing biological knowledge of the diseases. She is specifically interested in development of novel analytical methods for single-cell RNA sequencing data, spatial transcriptomic data, drug perturbation data and integration of different omics data. In this talk, she will introduce a recently developed method for spatial domain identification using large language model.

Single-cell and spatial methods to dissect complex diseases
2026-08-17 13:00 - 14:00
Judith and David Coffey SeminarSpeaker: Dr Boxiang Liu, National University of Singapore

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

Genome-wide association studies have identified more than a million risk variants for complex diseases, yet fewer than 5% of complex disease loci have validated target genes. Closing this gap requires resolving genetic regulation at the right cell types, ancestries, and cellular contexts. In this talk, I will present three complementary efforts from my lab. First, AIDA — a single-cell atlas of ~1 million PBMCs from ~500 donors of diverse Asian descent profiled with 5′ chemistry — captures 4.3-fold more splice junctions than prior 3′ libraries and reveals ancestry-biased splicing events, including an Asian-specific TCHP variant that modulates Graves’ disease risk. Second, we developed ISSAC to map cell-state-dependent sQTLs across millions of cells and uncovers Alzheimer’s-biased sQTLs in dorsolateral prefrontal cortex snRNA-seq. Third, we developed DIRAC to use adversarial domain-invariant representations to harmonize spatial multi-omic datasets, enabling a high-resolution T cell development atlas in the mouse thymus. Together, these methods chart a path from population-scale genetics to cell-state- and tissue-resolved mechanisms underlying complex diseases.

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Dr Boxiang Liu

As a PI and the director of the Genomic Data Science Lab in the National University of Singapore (www.boxiangliulab.com), Dr. Boxiang Liu advances the understanding of complex human diseases through innovative genetic, single-cell, and spatial transcriptomic analyses. He has published >90 publications, including Nature, Cell, Nature Genetics, and Nature Methods, focusing on genomic and transcriptomic methodologies to dissect the genetic architecture of polygenic diseases. A significant facet of his academic endeavour includes a pivotal role in the Genotype-Tissue Expression (GTEx) Project, contributing to a comprehensive understanding of genetic effects on molecular phenotype across diverse tissues. Additionally, he spearheaded the single-cell splicing analysis within the Asian Immune Diversity Atlas project, which collected >500 donors of diverse Asian ancestries. His group’s work provided the first cell-type-specific sQTL map using over 1 million PBMC single cells. He has been awarded the Presidential Young Professorship (Singapore), National Academy of Science Young Scientist Award (Singapore), National Research Foundation Fellow (Singapore), President’s Award in Natural Sciences and Mathematics (US), Charles B. Carrington Memorial Award (US), and the National Award for Outstanding Overseas Ph.D. Students (China).

Connect with Boxiang:X: @boxiangliuBluesky: @boxiangliu.bsky.social
