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X-ORIGINAL-URL:https://spds.sydney.edu.au
X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260413T130000
DTEND;TZID=Australia/Sydney:20260413T140000
DTSTAMP:20260420T045731Z
CREATED:20260327T034950Z
LAST-MODIFIED:20260420T045731Z
UID:4838-1776085200-1776088800@spds.sydney.edu.au
SUMMARY:Characterizing cell-type spatial relationships across length scales in spatially resolved omics data
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Rafael dos Santos Peixoto\, Johns Hopkins University \n\n\n\nThis was an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\n\n\nSpatially 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRafael dos Santos Peixoto\n\n\n\nRafael 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/
URL:https://spds.sydney.edu.au/event/characterizing-cell-type-spatial-relationships-across-length-scales-in-spatially-resolved-omics-data/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260420T130000
DTEND;TZID=Australia/Sydney:20260420T140000
DTSTAMP:20260420T044255Z
CREATED:20260330T021527Z
LAST-MODIFIED:20260420T044255Z
UID:4848-1776690000-1776693600@spds.sydney.edu.au
SUMMARY:Integrative transcriptome-based drug repurposing in tuberculosis
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Kewalin Samart\, University of Colorado \n\n\n\nThis was an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\n\n\nTuberculosis (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. \n\n\n\nHere\, 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. \n\n\n\nThis 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nKewalin Samart\n\n\n\nKewalin 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
URL:https://spds.sydney.edu.au/event/integrative-transcriptome-based-drug-repurposing-in-tuberculosis/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260504T130000
DTEND;TZID=Australia/Sydney:20260504T140000
DTSTAMP:20260508T052227Z
CREATED:20260417T014417Z
LAST-MODIFIED:20260508T052227Z
UID:4861-1777899600-1777903200@spds.sydney.edu.au
SUMMARY:PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Weize Xu\, Stanford University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nThe 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Weize Xu\n\n\n\nDr. 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
URL:https://spds.sydney.edu.au/event/pantheonos-an-evolvable-multi-agent-framework-for-automatic-genomics-discovery/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260511T130000
DTEND;TZID=Australia/Sydney:20260511T140000
DTSTAMP:20260515T012415Z
CREATED:20260424T020750Z
LAST-MODIFIED:20260515T012415Z
UID:4890-1778504400-1778508000@spds.sydney.edu.au
SUMMARY:Coupling Single-cell Genome & Epigenome to Study Functional Consequence of Somatic SVs
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Hyobin Jeong\, Yonsei University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSomatic 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Hyobin Jeong\n\n\n\nDr 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.
URL:https://spds.sydney.edu.au/event/coupling-single-cell-genome-epigenome-to-study-functional-consequence-of-somatic-svs/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260518T130000
DTEND;TZID=Australia/Sydney:20260518T140000
DTSTAMP:20260522T023827Z
CREATED:20260424T022258Z
LAST-MODIFIED:20260522T023827Z
UID:4897-1779109200-1779112800@spds.sydney.edu.au
SUMMARY:Genetic regulation of human skeletal muscle: from bulk QTLs to single-nucleus resolution
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Wang Wenjing\, National University of Singapore \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nAlthough 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWenjing Wang\n\n\n\nWenjing 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. \n\n\n\nFind out more on LinkedIn: https://www.linkedin.com/in/wenjing-wang-40a67b18b/
URL:https://spds.sydney.edu.au/event/genetic-regulation-of-human-skeletal-muscle-from-bulk-qtls-to-single-nucleus-resolution/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260525T130000
DTEND;TZID=Australia/Sydney:20260525T140000
DTSTAMP:20260529T064822Z
CREATED:20260518T023913Z
LAST-MODIFIED:20260529T064822Z
UID:5129-1779714000-1779717600@spds.sydney.edu.au
SUMMARY:Generalized cell phenotyping for spatial proteomics with language-informed vision models
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Xuefei (Julie) Wang\, California Institute of Technology \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nWe 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nXuefei (Julie) Wang\n\n\n\nXuefei (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/
URL:https://spds.sydney.edu.au/event/generalized-cell-phenotyping-for-spatial-proteomics-with-language-informed-vision-models/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260601T130000
DTEND;TZID=Australia/Sydney:20260601T140000
DTSTAMP:20260605T040625Z
CREATED:20260417T015751Z
LAST-MODIFIED:20260605T040625Z
UID:4869-1780318800-1780322400@spds.sydney.edu.au
SUMMARY:Through the Unlabeled Lens of Spatial Multi-Omics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Anthony A. Fung\, Yale University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSometimes 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Anthony A. Fung\n\n\n\nDr 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/
URL:https://spds.sydney.edu.au/event/through-the-unlabeled-lens-of-spatial-multi-omics/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260615T130000
DTEND;TZID=Australia/Sydney:20260615T140000
DTSTAMP:20260619T105424Z
CREATED:20260601T020224Z
LAST-MODIFIED:20260619T105424Z
UID:5173-1781528400-1781532000@spds.sydney.edu.au
SUMMARY:Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Long Yuan\, Johns Hopkins University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nIn 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLong Yuan\n\n\n\nLong 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
URL:https://spds.sydney.edu.au/event/reconstructing-biologically-coherent-cellular-profiles-from-imaging-based-spatial-transcriptomics/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260622T130000
DTEND;TZID=Australia/Sydney:20260622T140000
DTSTAMP:20260629T042022Z
CREATED:20260616T235053Z
LAST-MODIFIED:20260629T042022Z
UID:5203-1782133200-1782136800@spds.sydney.edu.au
SUMMARY:Statistical Brain Network Analysis: Recent Developments and Future Directions
DESCRIPTION:Judith and David Coffey SeminarSpeaker: Prof Sean L. Simpson\, Wake Forest University \n\n\n\nThis was a hybrid event. In-person in the Mackenzie Seminar Room\, Level 6\, Charles Perkins Centre and online via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nThe 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProf Sean L. Simpson\n\n\n\nProf 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. 
URL:https://spds.sydney.edu.au/event/statistical-brain-network-analysis-recent-developments-and-future-directions/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260728T140000
DTEND;TZID=Australia/Sydney:20260729T170000
DTSTAMP:20260507T235521Z
CREATED:20260507T235254Z
LAST-MODIFIED:20260507T235521Z
UID:4934-1785247200-1785344400@spds.sydney.edu.au
SUMMARY:Microcredential: Data Analysis for Precision Health
DESCRIPTION: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. \n\n\n\nThis 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. \n\n\n\nReal health and medical data will be utilised to explore common challenges\, practical workarounds\, and translate data into actionable insights. \n\n\n\nBy the end of this course\, you will be able to:\n\n\n\n\nformulate and interpret appropriate linear models to describe the relationships between multiple factors\n\n\n\ntrain and evaluate logistic regression models for binary data\n\n\n\nunderstand and apply linear mixed effect models for data with repeated measures\n\n\n\nvisualise survival data with Kaplan-Meier curves and perform inference with Cox proportional hazards models.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAims\n\n\n\nThis course will give participants the necessary understanding and skills to perform statistical analyses on health and medical data. \n\n\n\nParticipants will gain experience in working with real data and develop critical thinking skills to address common challenges. \n\n\n\nParticipants will learn to communicate their data and findings through graphical and statistical summaries. \n\n\n\nContent\n\n\n\nThe course covers four main topics: \n\n\n\n\nLinear models: Fit\, refine\, interpret and visualise regression models. We will discuss making predictions\, evaluating model fit and feature selection.\n\n\n\nLogistic regression: Interpret odds ratios to evaluate risk factors\, assess model performance when working with binary health outcomes.\n\n\n\nMixed effect models: Analyse repeated measures and hierarchical data to understand individual and group-level patterns in health and medical contexts.\n\n\n\nSurvival 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.
URL:https://spds.sydney.edu.au/event/microcredential-data-analysis-for-precision-health/
LOCATION: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
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DTSTART;TZID=Australia/Sydney:20260803T130000
DTEND;TZID=Australia/Sydney:20260803T140000
DTSTAMP:20260720T002518Z
CREATED:20260719T235602Z
LAST-MODIFIED:20260720T002518Z
UID:5282-1785762000-1785765600@spds.sydney.edu.au
SUMMARY:Interpretable\, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Jia Zhao\, Yale University \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nRecent 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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Jia Zhao\n\n\n\nDr 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
URL:https://spds.sydney.edu.au/event/interpretable-flexible-and-spatially-aware-integration-of-multiple-spatial-transcriptomics-datasets-from-diverse-sources/
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DTSTART;TZID=Australia/Sydney:20260810T130000
DTEND;TZID=Australia/Sydney:20260810T140000
DTSTAMP:20260724T064522Z
CREATED:20260724T064257Z
LAST-MODIFIED:20260724T064522Z
UID:5294-1786366800-1786370400@spds.sydney.edu.au
SUMMARY:SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Xiting Yan\, Yale University \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nSpatially 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.  \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Xiting Yan\n\n\n\nDr 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.
URL:https://spds.sydney.edu.au/event/speak-spatial-prompting-with-expert-aligned-knowledge-for-tissue-domain-identification-in-spatial-transcriptomics/
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DTSTART;TZID=Australia/Sydney:20260817T130000
DTEND;TZID=Australia/Sydney:20260817T140000
DTSTAMP:20260731T005319Z
CREATED:20260731T004352Z
LAST-MODIFIED:20260731T005319Z
UID:5405-1786971600-1786975200@spds.sydney.edu.au
SUMMARY:Single-cell and spatial methods to dissect complex diseases
DESCRIPTION:Judith and David Coffey SeminarSpeaker: Dr Boxiang Liu\, National University of Singapore \n\n\n\nThis 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 \n\n\n\n\n\n\n\n\n\nGenome-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. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Boxiang Liu\n\n\n\nAs 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). \n\n\n\nConnect with Boxiang:X: @boxiangliuBluesky: @boxiangliu.bsky.social
URL:https://spds.sydney.edu.au/event/single-cell-and-spatial-methods-to-dissect-complex-diseases/
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