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DTSTART;TZID=Australia/Sydney:20250303T130000
DTEND;TZID=Australia/Sydney:20250303T140000
DTSTAMP:20260415T182005
CREATED:20250219T032342Z
LAST-MODIFIED:20250509T062247Z
UID:2478-1741006800-1741010400@spds.sydney.edu.au
SUMMARY:Zero-Shot Foundation Model for a Universal Gene Expression Atlas of Human Tissue: Unveiling Clinically Relevant Cell States and Disease-Specific Spatial Niches
DESCRIPTION:Statistical Bioinformatics SeminarDr Xiaomeng Wan\, HKUST\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe rapid accumulation of single-cell datasets from diverse organs and tissues presents significant opportunities for understanding complex diseases\, yet challenges remain in effectively analyzing this wealth of information and further leveraging it to various data types\, including spatial transcriptomics (ST) and bulk RNA-seq datasets. Here\, we introduce UniGeneX\, a generative single-cell foundation model designed to reconstruct a universal gene expression profile from extensive transcriptomic data. UniGeneX minimizes batch effects while preserving biological variability\, enabling the identification of shared gene programs across tumor samples. By providing consistent cell type labels and leveraging biological patterns from training data\, UniGeneX facilitates the discovery of disease-specific cell niches in spatial and key cell states associated with clinical outcomes. Our model addresses existing limitations in current single-cell foundation models by focusing on a universal gene expression framework rather than merely learning embeddings for downstream tasks. We demonstrate the effectiveness of UniGeneX in characterizing disease-relevant cell states in glioma and idiopathic pulmonary fibrosis (IPF)\, ultimately advancing our understanding of the mechanisms underlying complex diseases. \n\n\n\n\n\nThis was an online event held via Zoom.\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\n\n\n\n\nDr Xiaomeng Wan\n\n\n\nDr Xiaomeng Wan is currently a Postdoctoral Associate in the Department of Mathematics at the Hong Kong University of Science and Technology (HKUST)\, under the guidance of Prof Can Yang. She earned her PhD from HKUST under the mentorship of Prof Can Yang. Her research centres on statistical machine learning and deep learning\, particularly exploring their applications in the analysis of transcriptomics datasets.
URL:https://spds.sydney.edu.au/event/zero-shot-foundation-model-for-a-universal-gene-expression-atlas-of-human-tissue-unveiling-clinically-relevant-cell-states-and-disease-specific-spatial-niches/
CATEGORIES:Videos
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/01/Complex-systems-1-scaled.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250310T130000
DTEND;TZID=Australia/Sydney:20250310T140000
DTSTAMP:20260415T182005
CREATED:20250219T030630Z
LAST-MODIFIED:20250509T062239Z
UID:2471-1741611600-1741615200@spds.sydney.edu.au
SUMMARY:Explaining the asynchrony of aging through cell population dynamics
DESCRIPTION:Statistical Bioinformatics SeminarDr Ming Yang\, HKUST\n\n\n\n\n\n\n\n\n\n\n\n\n\nOrgans and tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels\, from functional hallmarks\, such as anatomical structures and physiological processes\, to molecular endophenotypes\, such as the transcriptome and metabolome. However\, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves\, here we test the hypothesis that demographic differences among cell types\, determined by cell-specific differences in turnover rate\, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages\, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast\, in short-lived cell types\, the transcriptome is enriched for genes associated with DNA repair. Based on these observations\, we develop a novel heuristic model that explains how and why aging rates differ among cell types. \n\n\n\n\n\nThis is an online event held via Zoom.\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\n\n\n\n\nDr Ming Yang\n\n\n\nMing Yang is a Research Assistant Professor in the Division of Life Science at the Hong Kong University of Science and Technology (HKUST). She earned her Ph.D. in Biochemistry and Molecular Biology from Sun Yat-sen University in 2017\, with a training background in population genetics and bioinformatics. From 2017 to 2022\, she was a Postdoctoral Research Scientist at the University of Washington School of Medicine\, and later promoted to an Acting Instructor. In 2024\, she joined HKUST\, focusing on the application of computational and systems biology in aging and aging-related diseases.
URL:https://spds.sydney.edu.au/event/explaining-the-asynchrony-of-aging-through-cell-population-dynamics/
CATEGORIES:Videos
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250317T130000
DTEND;TZID=Australia/Sydney:20250317T140000
DTSTAMP:20260415T182005
CREATED:20250226T014312Z
LAST-MODIFIED:20250509T062231Z
UID:2608-1742216400-1742220000@spds.sydney.edu.au
SUMMARY:Multi-state evolutionary model quantifies tumour cellular plasticity
DESCRIPTION:Statistical Bioinformatics SeminarDr Gladys Poon\, HKU\n\n\n\n\n\n\n\n\n\n\n\n\n\nCell-state transition dynamics are important in many diseases in cancer\, transitions among distinct cell states can affect treatment effectiveness and metastasis. Cancer evolutionary studies based on phylogenetics often assume weak or no selection – especially in the recent past – to estimate effective population sizes and evolutionary forces across a relatively long time span. However\, this assumption is violated during tumour growth when cells rapidly proliferate and outcompete one another under strong selection pressures.We adopt a stochastic modelling approach where cells divide according to a birth-death branching process and couple their fates with a Markov model for phenotypic transitions. Both genotype and phenotype are simultaneously inherited by the next generation. Phylogenies are constructed for a subsampled population of cells and coupled cell state information at a single time-point is used to estimate transition rates. We show that it is possible to determine phenotypic transition dynamics for specific population trajectories by ‘fine-graining’ node depth levels. This feature is crucial for addressing the proliferation of tumours which are mixtures of clones with different selection advantages.We pair our analysis with cell state annotations derived specifically for single-cell RNAsequencing (scRNAseq) data to define evolutionary relatedness between cell phenotypes. We then apply our computational framework to published metastatic pancreatic cancer phylogenies reconstructed using CRISPR-based lineage tracing in mice\, where scRNAseq information is available for each leaf. By comparing across metastatic locations in a single mouse\, we are able to reveal changes in selection pressures and cell phenotypic transition rates during metastatic progression. Inferred cell state transitions are supported by inferred RNA velocities. \n\n\n\n\n\nThis is an online event held via Zoom.\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\n\n\n\n\nDr Gladys Poon\n\n\n\nDr Gladys Poon completed both her undergraduate (Physics) and postgraduate (PhD in Oncology) studies at the University of Cambridge. During her time in the Blundell lab\, Gladys used population genetics to quantify the expected mutation burden and unknown drivers in human cancers\, particularly in acute myeloid leukemia. She also combined newly gained experimental expertise with her quantitative background to interrogate clonal evolutionary patterns in leukemic bone marrow\, revealing levels of positive selection during the human lifespan. Her research bridged between mathematics\, cancer genomics and computational modelling. \n\n\n\nGladys is now with the SMA lab at HKU – working on cellular plasticity in hepatocellular carcinoma. This lab uses lineage tracing experiments in mice and modelling to understand the role of cellular plasticity during tumor evolution.
URL:https://spds.sydney.edu.au/event/multi-state-evolutionary-model-quantifies-tumour-cellular-plasticity/
CATEGORIES:Videos
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250324T130000
DTEND;TZID=Australia/Sydney:20250324T140000
DTSTAMP:20260415T182005
CREATED:20250312T011044Z
LAST-MODIFIED:20250509T062218Z
UID:2919-1742821200-1742824800@spds.sydney.edu.au
SUMMARY:Towards Generalist AI Models in Pathology: The Unique Role of Molecular Data
DESCRIPTION:Statistical Bioinformatics SeminarDr Guillaume Jaume\, Harvard Medical School\n\n\n\n\n\n\n\n\n\n\n\n\n\nHow can we develop generalist AI models for pathology? How can we leverage these models for better diagnosis\, prognosis\, response-to-treatment prediction\, and biomarker discovery? Foundation models have taken the field of computational pathology by storm—bringing a whole new perspective on AI model development\, training\, and evaluation. Whole-slide image classification now largely relies on pretrained “patch encoders”\, such as UNI\, and increasingly relies on “slide encoders”\, such as Threads. Multimodal learning\, in particular based on morphomolecular data\, emerges as a critical component for training and evaluating these models. In this talk\, I will present our recent works in this direction: (1) HEST (NeurIPS’24) for joint analysis of spatial transcriptomics and histology\, and (2) Tangle (CVPR’24)\, Madeleine (ECCV’24) and Threads (in review) for molecular-guided slide representation learning. I will close by sharing my perspective on the potential future direction of the field. \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 Guillaume Jaume\n\n\n\nGuillaume is a 3rd-year postdoctoral researcher at Harvard Medical School and Brigham & Women’s Boston Hospital in the group of Prof. Faisal Mahmood. He obtained his Ph.D. in Electrical and Electronic Engineering from EPFL in collaboration with IBM Research and ETH Zurich in 2022. Guillaume’s research focuses on computational pathology to integrate AI tools into the clinical and research facets of pathology. His research involves two main objectives: first\, enhancing the representation learning of tissue by developing general-purpose foundation models for pathology and oncology; and second\, integrating AI tools in drug development to improve drug safety assessment\, detect toxicity\, and discover safety biomarkers.
URL:https://spds.sydney.edu.au/event/towards-generalist-ai-models-in-pathology-the-unique-role-of-molecular-data/
CATEGORIES:Videos
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250331T130000
DTEND;TZID=Australia/Sydney:20250331T140000
DTSTAMP:20260415T182005
CREATED:20250324T225435Z
LAST-MODIFIED:20250627T014800Z
UID:3073-1743426000-1743429600@spds.sydney.edu.au
SUMMARY:Revisiting quality control\, normalisation\, and spatially variable gene calling in CosMx WTx
DESCRIPTION:Dr Dharmesh Bhuva\, University of Queensland \n\n\n\n\n\n\n\n\n\nSpatially resolved molecular measurements have revolutionised the study of disease systems\, providing an unprecedented resolution and throughput of molecular measurements. The opportunity posed by such data requires a new set of tools to unlock its true potential. Initial uptake of this data saw the repurposing of computational tools developed for single-cell RNA-seq\, however\, new studies are showing the need for a different paradigm when analysing spatial measurements. While measurements across cells are relatively independent in single-cell RNA-seq\, they are spatially autocorrelated in spatial RNA-seq. I will begin by demonstrating the presence of such autocorrelation and show that single-cell inspired normalisation strategies are detrimental to spatial datasets. Next\, I will present some of our ideas on quality control of such data\, focusing on the CosMx platform. I will then present our SpaNorm model for normalisation of spatial molecular measurements and demonstrate extensions of this model that allow SVG calling and a GLM-PCA approximation. While the ideas presented in this talk have been assessed in CosMx data\, they should be applicable across other imaging-based spatial transcriptomics platforms. \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 Dharmesh Bhuva\n\n\n\nDr Dharmesh D Bhuva is an early-career computational systems biologist who is passionate about understanding how complex systems of gene regulation and signalling lead to diverse molecular phenotypes in healthy and diseased tissues. He completed his PhD in 2020 at the University of Melbourne and WEHI where he developed new systems biology approaches to study molecular function and gene regulation in cancer. He then undertook his post-doctoral studies at the world-renowned WEHI Bioinformatics division\, where he embarked on developing novel approaches to study cancer tissues using spatial molecular data. In 2023\, he joined the computational systems oncology division at the South Australian Immunogenomics Cancer Institute (SAiGENCI) to continue his cutting-edge research in developing computational approaches to study tissue architecture. Dr. Bhuva has recently been awarded a MRFF grant and a NHMRC investigator grant (EL1) to identify spatial biomarkers in cancer systems\, work which he will undertake at the Frazer Institute.
URL:https://spds.sydney.edu.au/event/revisiting-quality-control-normalisation-and-spatially-variable-gene-calling-in-cosmx-wtx/
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