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DTSTART;TZID=Australia/Sydney:20260728T140000
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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
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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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