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X-WR-CALNAME:Sydney Precision Data Science Centre
X-ORIGINAL-URL:https://spds.sydney.edu.au
X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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DTSTART;TZID=Australia/Sydney:20260622T130000
DTEND;TZID=Australia/Sydney:20260622T140000
DTSTAMP:20260629T042022Z
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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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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
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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
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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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