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X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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DTSTART;TZID=Australia/Sydney:20260907T130000
DTEND;TZID=Australia/Sydney:20260907T140000
DTSTAMP:20260824T002437Z
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SUMMARY:Single-cell spatial pharmacobiology identifies conserved stromal barriers to therapeutic antibody delivery in human solid tumors
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Guolan Lu\, Stanford 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\nThe development of effective antibody therapeutics has been hampered by a lack of methods to measure drug delivery and activity within tumors at single-cell resolution. Here we introduce single-cell spatial pharmacobiology (SSP)\, an experimental and analytical framework that integrates in situ imaging of a systemically infused\, fluorescently labeled therapeutic antibody with high-plex spatial proteomics to quantify antibody distribution\, target engagement and tumor microenvironment (TME) architecture. We applied SSP to tumor tissues from participants with head and neck squamous cell carcinoma and pancreatic ductal adenocarcinoma who received the antibody panitumumab-IRDye800 in phase 1 trials. SSP identified pronounced spatial heterogeneity in single-cell drug delivery and target engagement\, shaped by conserved stromal barriers\, including periostin-rich extracellular matrix assemblies and fibroblast-activation-protein-positive cancer-associated fibroblast neighborhoods\, which were associated with reduced antibody delivery in both tumor types. SSP measures drug–target–TME interactions in human tumors and can support studies of resistance mechanisms\, dosing strategies and discovery of spatial biomarkers for precision oncology. \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 Guolan Lu\n\n\n\nGuolan Lu is a tenure-track Assistant Professor at Stanford University. Her laboratory develops spatial multi-omics and artificial intelligence technologies to determine how tissue organization shapes disease progression and therapeutic response. During her postdoctoral training at Stanford with Eben Rosenthal and Garry Nolan\, she designed and led a first-in-human fluorescence-guided surgery study in pancreatic cancer and pioneered single-cell spatial pharmacobiology in human tumors. She received her PhD through the joint Biomedical Engineering program at Georgia Tech and Emory University. \n\n\n\nConnect with Guolan:X: @GuolanLu
URL:https://spds.sydney.edu.au/event/single-cell-spatial-pharmacobiology-identifies-conserved-stromal-barriers-to-therapeutic-antibody-delivery-in-human-solid-tumors/
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DTSTART;TZID=Australia/Sydney:20260914T130000
DTEND;TZID=Australia/Sydney:20260914T140000
DTSTAMP:20260903T020427Z
CREATED:20260902T015611Z
LAST-MODIFIED:20260903T020427Z
UID:5473-1789390800-1789394400@spds.sydney.edu.au
SUMMARY:Joint PhD student presentations
DESCRIPTION:This is a hybrid event with two presentations. In person venue: Carslaw Lecture Theatre 373Zoom link: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nInterpretable analytical methods for characterising disease-associated phenotypic modulation of cellsSpeaker: Elijah Willie\, University of Sydney\n\n\n\nRealising the analytical potential of single-cell technologies requires computational frameworks that address fundamental challenges in characterising cellular heterogeneity and disease-associated phenotypes. High-throughput cytometry and spatial omics platforms have advanced substantially in their resolution and throughput\, enabling simultaneous measurement of dozens of parameters across millions of cells. However\, these technologies present analytical obstacles including technical artifacts\, batch effects\, and the complexity of spatial interactions that limit biological discovery and clinical application. Addressing challenges such as robust cell type identification\, cross-cohort transferability\, and spatial variance decomposition necessitates the development of innovative computational approaches. \n\n\n\nTo this end\, this work contributes to single-cell computational biology by (1) establishing a multiview framework that harmonizes correlation and distance metrics through ensemble learning\, overcoming similarity metric limitations in imaging cytometry to enable robust identification of cellular phenotypes\, (2) developing transferable deep learning approaches combining batch-agnostic normalization with hierarchically-structured feature selection to enable cross-institutional validation of cytometry-based biomarkers\, and (3) creating variance decomposition methods for spatial transcriptomics that quantify cell type-specific interactions while correcting for lateral spillover\, revealing how proximity modulates gene expression programs in breast cancer and melanoma. The frameworks presented here provide validated computational tools that advance single-cell analysis and establish foundations for future development of interpretable methods that characterise disease-associated cellular modulation. \n\n\n\n\n\n\n\nMultimodal learning for organ transplant diagnosticsSpeaker: Harry Robertson\, University of Sydney\n\n\n\nKidney transplantation remains the only solution for end-stage renal failure\, yet chronic immune injury and reactive monitoring limit long-term graft survival and patient quality of life. Current strategies to monitor patients are reactive and require invasive biopsies to diagnose allograft dysfunction. These biopsies are assessed by semi-quantitative criteria with known between-reader variability. This thesis develops 1) a blood test for allograft rejection and 2) a co-pilot for renal biopsy assessment. To develop our blood test\, we first assembled PROMAD\, the largest transcriptomic resource in the field\, spanning more than 150 studies and 12\,000 samples across kidney\, heart\, liver and lung transplantation. By developing TOP\, a novel transfer learning framework\, we produced an organ-agnostic blood test that predicts biopsy-proven rejection (AUC=0.81)\, outperforming the current standard-of-care. Next\, we developed RenalAgent an AI co-pilot that reads kidney biopsies\, scores lesions and produces evidence-linked reports. Across 15\,082 biopsies from Mass General Brigham (MGB)\, RenalAgent achieved high concordance with expert pathologists (mean kappa=0.73 across 16 tasks). In external cohorts from Australia\, Europe\, and Mt Sinai (US)\, RenalAgent demonstrated prognostic utility through longitudinal assessment\, predicting graft loss better than the current standard-of-care. Together\, these contributions aim to advance precision monitoring for transplant recipients. \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\nElijah Willie\n\n\n\nElijah studied computer science and molecular biology as an undergraduate at Simon Fraser University\, then completed a master’s in bioinformatics at the University of British Columbia. After a few years working as a bioinformatician\, he began his PhD at the University of Sydney in 2022 and completed it in 2025. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHarry Robertson\n\n\n\nHarry completed his undergraduate degree in applied medical science at the University of Sydney in 2021. During his PhD he received a Westpac Future Leaders Scholarship\, and in 2024 a Fulbright Future Scholarship to study under Prof. Faisal Mahmood at Harvard. His work has been published in Nature Medicine.
URL:https://spds.sydney.edu.au/event/statistical-bioinformatics-seminar/
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DTSTART;TZID=Australia/Sydney:20260921T130000
DTEND;TZID=Australia/Sydney:20260921T140000
DTSTAMP:20260914T031739Z
CREATED:20260914T031524Z
LAST-MODIFIED:20260914T031739Z
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SUMMARY:STORM: Unified spatial\, temporal\, and multimodal data integration into biologically interpretable embeddings
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Xinyi Lisa Chen\, 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\nSpatiotemporal multi-omic atlases are rapidly emerging and shifting tissue biology from static\, single-modality snapshots to dynamic\, multi-layer atlases across development and disease. Yet integrating space\, time\, and modality while preserving tissue geometry\, transient niches\, and real biological change remains difficult. Here we introduce STORM (Spatial Temporal multi-Omics Representation Model)\, among the first end-to-end frameworks to jointly model space–time–modality while grounding the representation in gene programs. STORM learns a unified cell embedding using a multimodal graph neural network variational autoencoder\, jointly aligning timepoints and modalities while correcting time-dependent technical variation. Leveraging curated gene programs\, STORM decomposes complex biology into interpretable\, program-level components across modalities and reveals time-evolving program dynamics. STORM extends to multiple timepoints\, sequencing platforms\, tissues\, and biological conditions\, as demonstrated in three paired spatial RNA–ATAC datasets spanning postnatal (P0–P22) and embryonic (E11.0–E18.5) mouse brain development and a lysolecithin-induced demyelination model. Across datasets\, STORM preserves stage-dependent anatomy\, resolves small anatomically meaningful populations (including the island of Calleja)\, tracks coordinated spatiotemporal program trajectories\, and enables program-level RNA–ATAC contrasts that expose regulatory priming not apparent from expression alone. Our results demonstrate that STORM provides a scalable\, interpretable route to dissect spatiotemporal molecular programs from time-course spatial multi-omic 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\nXinyi Lisa Chen\n\n\n\nXinyi Lisa Chen is a Ph.D. candidate in Computational Biology and Biomedical Informatics at Yale University\, advised by Professor Hongyu Zhao. Before Yale\, she earned her bachelor’s degree in Computer Science and Statistics from the University of Toronto. Her research develops interpretable machine learning methods for spatial and perturbational multi-omics data\, with the broader goal of building computational models of a “virtual cell in tissue.” She is the lead developer of STORM\, a graph-based generative framework that integrates spatial context\, developmental time\, and multi-modal measurements (e.g. RNA and ATAC) into biologically interpretable gene-program representations. She has also led multimodal single-cell analyses of cellular and regulatory changes across prodromal and early Parkinson’s disease. More recently\, Lisa has expanded her work to scientific AI agents\, studying reliable tool use\, long-horizon evaluation\, experience memory reuse\, and failure monitors for AI agents.  \n\n\n\nConnect with Xinyi Lisa:LinkedIn: www.linkedin.com/in/xinyi-lisa-chen-yale
URL:https://spds.sydney.edu.au/event/storm-unified-spatial-temporal-and-multimodal-data-integration-into-biologically-interpretable-embeddings/
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DTSTART;TZID=Australia/Sydney:20260928T130000
DTEND;TZID=Australia/Sydney:20260928T140000
DTSTAMP:20260914T032727Z
CREATED:20260914T032605Z
LAST-MODIFIED:20260914T032727Z
UID:5507-1790600400-1790604000@spds.sydney.edu.au
SUMMARY:Powerful and accurate case-control analysis of spatial molecular data
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Yakir Reshef\, Harvard Medical School \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\nAs spatial molecular data grow in scope\, there is a pressing need to identify disease-associated spatial structures. Current approaches typically make restrictive assumptions such as representing tissue regions by abundances of discrete cell types and samples by abundances of discrete niches; this risks overlooking important signals. I will discuss variational inference-based microniche analysis (VIMA)\, a method combining deep learning with principled statistics to discover disease-associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to summarize the contents of every small tissue patch in a dataset via numeric “fingerprints”. It uses these to define many data-dependent\, overlapping “microniches” and meta-analyzes them to identify microniches whose abundance correlates significantly with case-control status. After describing the method\, I’ll show how it performs in simulations designed to assess calibration\, power\, and spatial accuracy. I’ll then give some examples of how we have used VIMA on spatial datasets from diverse spatial modalities\, where it recapitulates known biology and identifies novel spatial features of disease. \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 Yakir Reshef\n\n\n\nYakir Reshef completed his undergraduate training in mathematics at Harvard College and subsequently completed a Fulbright in probability theory followed by a PhD in computer science at Harvard University and an MD from Harvard Medical School. He is currently a faculty member at Harvard Medical School and Brigham and Women’s Hospital\, where his group focuses on developing and applying new methods to achieve a quantitative understanding of inflammation and auto-immunity from high-dimensional molecular data such as single-cell and spatial transcriptomics data. He is also clinically active\, treating patients with autoimmune disease as a rheumatologist. \n\n\n\nConnect with Yakir:X: @YakirReshefBluesky: @yakirreshef.bsky.social
URL:https://spds.sydney.edu.au/event/powerful-and-accurate-case-control-analysis-of-spatial-molecular-data/
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DTSTART;TZID=Australia/Sydney:20261015T110000
DTEND;TZID=Australia/Sydney:20261015T120000
DTSTAMP:20260909T020338Z
CREATED:20260909T015737Z
LAST-MODIFIED:20260909T020338Z
UID:5483-1792062000-1792065600@spds.sydney.edu.au
SUMMARY:Charles Perkins Centre Data Science Hub - Introduction and Showcase
DESCRIPTION:This event is in-person only and requires registration. \n\n\n\n\n\n\n\nJoin us as we introduce the Charles Perkins Centre Data Science Hub and showcase our first projects.Discover how the Data Science Hub’s collaborative platform advances data-intensive research\, builds capacity and shares knowledge. The event will highlight key achievements from projects of varying scale and share our vision to support EMCRs and accelerate research translation.We invite you to meet our data scientists\, explore opportunities for collaboration\, and discover how to engage with us.  Refreshments will be provided. \n\n\n\n\n\n\n\nThe Data Science Hub is a collaborative partnership with the Charles Perkins Centre and Sydney Precision Data Science Centre\, co-funded by the Charles Perkins Centre’s Jennie Mackenzie Research Fund\, the Faculty of Medicine and Health and Faculty of Science.  The Data Science Hub advances data-intensive research with a direct translational impact in biomedical\, metabolomics health\, epidemiological research\, and beyond. We specialise in context-specific data analysis for high-throughput biomedical data generated by scientists.
URL:https://spds.sydney.edu.au/event/charles-perkins-centre-data-science-hub-introduction-and-showcase/
LOCATION:Jennie Mackenzie Room\, Level 6\, Charles Perkins Centre\, Johns Hopkins Drive\, University of Sydney\, Camperdown NSW 2006\, Sydney\, 2006\, Australia
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