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X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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DTSTART;TZID=Australia/Sydney:20260615T130000
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DTSTAMP:20260601T180435
CREATED:20260601T020224Z
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SUMMARY:Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Long Yuan\, Johns Hopkins 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\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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DTSTART;TZID=Australia/Sydney:20260728T140000
DTEND;TZID=Australia/Sydney:20260729T170000
DTSTAMP:20260601T180435
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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