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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20251110T080000
DTEND;TZID=Australia/Sydney:20251112T050000
DTSTAMP:20260405T161633
CREATED:20250324T213410Z
LAST-MODIFIED:20250725T033749Z
UID:1364-1762761600-1762923600@spds.sydney.edu.au
SUMMARY:Australian Data Science Network Conference 2025
DESCRIPTION:Sydney Precision Data Science Centre is delighted to host the 4th Australian Data Science Network (ADSN) conference. The ADSN conference aims to connect Australia’s top experts in data science\, fostering collaboration\, expanding opportunities\, and showcasing our collective capabilities. \n\n\n\n\nAustralian Data Science Network Conference 2025 Link\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDETAILS\n\n\n\nNovember 10-12 \n\n\n\n\n\n\n\nORGANISERS\n\n\n\n\n\nCharles Perkins Centre \n\n\n\nUniversity of Sydney \n\n\n\n\n\n\n\n\n\nORGANISERS\n\n\n\n\n\nAustralian Data Science Network \n\n\n\nUniversity of Sydney
URL:https://spds.sydney.edu.au/event/australian-data-science-network-conference-2025/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20251027T130000
DTEND;TZID=Australia/Sydney:20251027T140000
DTSTAMP:20260405T161633
CREATED:20251020T004350Z
LAST-MODIFIED:20251027T033711Z
UID:4394-1761570000-1761573600@spds.sydney.edu.au
SUMMARY:Harnessing the human microbiome for therapeutic purposes
DESCRIPTION:Statistical Bioinformatics SeminarDr Shanlin Ke\, The Ohio State 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 human microbiome plays a critical role in health and disease and represents a promising source for novel therapeutic strategies. In this talk\, I will first provide an overview of how microbiomes are currently leveraged for therapeutic purposes. I will then introduce a computational approach we have developed to uncover complex host–microbiota interactions. Using our recent PTSD–microbiome study as an example\, I will demonstrate how this approach can reveal host–microbiota interactions and be validated through animal models. Next\, I will present our AI-based framework for discovering antimicrobial peptides (AMPs) from the urinary microbiome\, highlighting its potential to combat antimicrobial resistance. I will conclude by discussing an idea: rejuvenating the human gut microbiome to preserve the “healthy” microbiome for future therapeutic use. \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 Shanlin Ke\n\n\n\nDr Shanlin Ke joined The Ohio State University as an Assistant Professor in May 2025 in the Division of Gastroenterology\, Hepatology\, and Nutrition within the Department of Internal Medicine. Dr Ke is a biologist with expertise in bioinformatics\, microbiome\, machine learning\, multi-omics\, and wet-lab techniques. He received his PhD in Animal Genetics from Jiangxi Agricultural University in 2020. In 2018\, he joined Dr Yang-Yu Liu’s lab at the Channing Division of Network Medicine\, Harvard Medical School as a visiting student. He continued his microbiome research as a postdoctoral fellow in Dr Liu’s lab beginning in 2021\, where he developed and applied computational approaches to study host–microbiota interactions in chronic conditions (e.g.\, inflammatory bowel disease and post-traumatic stress disorder) and infectious diseases (e.g.\, Clostridioides difficile infection and urinary tract infection). In parallel\, he conducted in vitro and in vivo experiments to validate computational findings and investigate the mechanisms underlying host-microbiota interactions in human diseases. \n\n\n\nAt OSU\, Dr Ke’s lab focuses on developing innovative methodologies and leveraging bioinformatics tools\, metagenomic sequencing\, machine learning\, and wet-lab techniques to investigate the role of the human microbiome in pancreatic diseases and to develop microbiome-based therapeutics.
URL:https://spds.sydney.edu.au/event/harnessing-the-human-microbiome-for-therapeutic-purposes/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20251020T130000
DTEND;TZID=Australia/Sydney:20251020T140000
DTSTAMP:20260405T161633
CREATED:20251013T013941Z
LAST-MODIFIED:20251013T013957Z
UID:4222-1760965200-1760968800@spds.sydney.edu.au
SUMMARY:High-resolution Characterization of Age-specific Changes in HPV-negative HNSCC through Building a scRNA-Sequencing Atlas
DESCRIPTION:Statistical Bioinformatics SeminarLina Kroehling\, Boston 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\nAge is strongly associated with both the incidence and mortality risk of head and neck cancer. While cancer and aging share many of their defining “hallmarks”\, including chronic inflammation\, increased genomic instability\, and increased senescence\, more research is needed to elucidate the specific mechanisms contributing to cancer aggressiveness in older patients. \n\n\n\nTo this end\, we have assembled a hiqh-quality human single-cell RNA-sequencing HNSCC atlas profiling more than 290\,000 cells across more than 70 patients\, with ages ranging between 18 and 90\, which provides a unique resource to investigate age-associated changes in the disease’s heterogeneity.  \n\n\n\nTo create the atlas\, we integrated seven publicly available single-cell RNAseq datasets from 73 HPV-negative patients. Cells were clustered\, classified\, and characterized by gene set enrichment analysis\, both in the epithelial cell compartment and in the tumor microenvironment (TME). Differential expression and cell type proportion analyses were performed to identify genes and cell type compositional changes associated with age. Cell cell communication analysis was performed to identify interacting cell types and modeled to identify specific ligands and receptors changing with age. \n\n\n\nWe identified distinct age-related changes in cell type composition\, including vascular endothelial cells increasing with age\, and several tumor clusters with distinct functions also changing with age. Further analyses are ongoing\, and we plan to functionally validate the hypotheses generated\, specifically the presence of differentially abundant cell populations\, and age-specific ligand-receptor signaling events that contribute to tumor growth. \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\nLina Kroehling\n\n\n\nLina Kroehling is a bioinformatics PhD candidate in the Monti Lab at Boston University where she utilizes multi-omics data to study Head and Neck Cancer. She has a B.S. and M.S. in biochemistry from Clark University in Worcester MA\, and worked as a bioinformatician in academic immunology labs before starting her PhD. 
URL:https://spds.sydney.edu.au/event/high-resolution-characterization-of-age-specific-changes-in-hpv-negative-hnscc-through-building-a-scrna-sequencing-atlas/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20251013T130000
DTEND;TZID=Australia/Sydney:20251013T140000
DTSTAMP:20260405T161633
CREATED:20251008T003009Z
LAST-MODIFIED:20251008T231811Z
UID:4216-1760360400-1760364000@spds.sydney.edu.au
SUMMARY:Using genomics-informed agent-based models to understand neoplastic phenotype transitions in the human PDAC microenvironment
DESCRIPTION:Statistical Bioinformatics SeminarDr Jeanette Johnson\, University of Maryland\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\nWe present a novel integration of data-driven single-cell analysis with mechanistic mathematical modeling to predict the impact of cancer-associated fibroblasts (CAF) on pancreatic ductal adenocarcinoma (PDAC) invasion. Bioinformatic analyses of high-throughput single-cell and spatial molecular assays give us access to human tumor gene expression at the time of measurement\, enabling direct characterization of the human tumor microenvironment (TME)\, complementing our understanding of cell behavior gained from preclinical models. Notably\, applying our Bayesian non-negative matrix factorization algorithm CoGAPS to single-cell RNA-seq data from PDAC allowed us to associate a gene program of concurrent epithelial-to-mesenchymal transition (EMT) and inflammation in epithelial cells with the presence of CAFs\, validated through organoid co-culture experiments. We hypothesize that this gene program represents a mechanism by which CAFs promote invasive behavior in neoplastic cells over time. Testing this hypothesis requires inference of temporal changes\, which is not possible with single-timepoint data collection in genomics data. In contrast to data-driven bioinformatics\, mechanistic mathematical modeling can capture processes that happen over time and make predictions about a system. Agent-based models (ABMs) are one form of such mechanistic mathematical models that are well suited for temporal modeling of cellular phenotypes. We developed new software for ABMs that abstract cells into software agents with individual states and their own rules of behavior from single-cell and spatial molecular assays for temporal inference. We apply this technique to understand how CAFs in the PDAC microenvironment support the series of phenotype transitions comprising tumor progression and invasion\, by encoding the CAF-induced ability of tumor cells to migrate and a cessation of proliferation in tumor cells\, induced by CAF-secreted\, non-diffusable substrate as inferred from our genomics analysis.  While this provides a virtual framework to test the impact of varying CAF density on tumor cell invasion\, it initially lacked integration with the spatial architecture of human tumors. We then apply the ABM to human PDAC spatial transcriptomics data to forecast tumor behavior over time in a spatially-informed setting\, varying the hypothesized mechanisms. This revealed that a substrate-driven mechanism results in a  broad mesenchymal-like zone surrounding epithelial-like outgrowths\, while a cell contact-mediated mechanism results in a single-cell-wide boundary surrounding more significant outgrowth. When we compared these to real tumor compositions\, this favors the explanation that secreted CAF substrate drives EMT in PDAC neoplastic cells\, which we then validate in organoids with CAF conditioned media. Our work demonstrates how integrating ABMs with genomic and spatial data enables testing of mechanistic hypotheses about tumor-stroma interactions and provides deeper insights into PDAC microenvironments\, potentially informing strategies to intercept disease progression. \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 Jeanette Johnson\n\n\n\nI am currently a postdoctoral fellow at the University of Maryland School of Medicine\, after completing my PhD in Immunology at Johns Hopkins this past May. My genomics obsession started as an undergraduate student at the University of British Columbia in computer science and immunology when I got to write processing scripts for some single-cell PBMC data. I will be presenting some of my PhD work which I did in Dr. Elana Fertig’s lab\, looking at ways to build agent-based computational models systematically from genomics data\, which I developed in the context of the pancreatic tumor microenvironment. Living in Baltimore\, Maryland with my two cats and partner\, my favorite hobby is convincing people to go eat seafood with me. I also love to do my nails and am actively searching for the maximally dramatic set that still lets me type and pipette.
URL:https://spds.sydney.edu.au/event/using-genomics-informed-agent-based-models-to-understand-neoplastic-phenotype-transitions-in-the-human-pdac-microenvironment/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250929T130000
DTEND;TZID=Australia/Sydney:20250929T140000
DTSTAMP:20260405T161633
CREATED:20250919T012153Z
LAST-MODIFIED:20251112T072543Z
UID:4181-1759150800-1759154400@spds.sydney.edu.au
SUMMARY:Open-ST: High-resolution spatial transcriptomics in 2D and 3D
DESCRIPTION:Statistical Bioinformatics SeminarDaniel León-Periñán\, Berlin Institute for Medical Systems BiologyDr Elena Splendiani\, Sapienza University of Rome\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSpatial transcriptomics (ST) has significantly advanced our understanding of the molecular mechanisms involved in tissue development\, homeostasis\, and disease. However\, there is a need for easy-to-use\, high-resolution and cost-efficient methods that can be scaled up for the analysis of tissues in 3D. To address these challenges\, we introduce Open-ST\, a high-resolution\, sequencing-based platform\, designed for the analysis of tissue molecular organization in both 2D and 3D. This experimental and computational resource is open-source\, modular and cost-effective\, making it accessible to a broad range of researchers and facilitates the adaptation of new implementations. \n\n\n\nOpen-ST has proven effective in various contexts. In mouse brain tissue\, it captured transcripts at subcellular resolution and successfully reconstructed cell types. In a primary head-and-neck tumor and patient-matched healthy and metastatic lymph nodes\, Open-ST captured the diversity of immune\, stromal\, and tumor populations\, findings that were corroborated by imaging-based ST. Notably\, distinct cellular states were organized around cell-cell communication hotspots in the primary tumor. These transcriptomic states were maintained in the metastasis\, despite the spatial organization being disrupted. Reconstructing the metastatic lymph node into a “3D virtual tissue block” from serial tissue sections\, identified spatially contiguous structures that were not discernible in 2D. These included potential biomarkers located at the 3D tumor/lymph node boundary. \n\n\n\nCurrently\, we are extending Open-ST to increase its applicability to a wider range of research questions\, from small RNAs to long-reads\, as well as extending its application to formalin-fixed paraffin-embedded tissues. Furthermore\, integrating immunofluorescence staining in the workflow allows a multiomic perspective on the tissue architecture. Given its accessibility and versatility\, Open-ST can be adopted and customized by a diverse range of users\, enabling its application to increasingly specialized studies. \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\nDaniel León-Periñán\n\n\n\nDani is a computational biologist pursuing his PhD in the Rajewsky lab at the Berlin Institute for Medical Systems Biology (BIMSB-MDC). His research focuses on understanding how gene expression patterns\, in space and time\, can predict disease progression\, by developing methods that generate\, analyze and integrate large-scale data. With a background in Biotechnology and Computer Science\, he works at the intersection of spatial biology\, data visualization\, and machine learning\, with the aim to make biological data accessible and searchable. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Elena Splendiani\n\n\n\nElena is a postdoctoral research fellow at the Department of Experimental Medicine\, Sapienza University of Rome. She received her PhD in Molecular Medicine from Sapienza University in 2023 and her Master’s degree in Genetics and Molecular Biology in 2019 from the same institution. Her research focuses on solid tumors\, with a particular interest in biomarker discovery and RNA-level molecular characterization\, including both coding and non-coding RNAs. She co-developed a high-spatial-resolution technology\, Open-ST\, which she is currently applying to characterize tumors and study their microenvironments.
URL:https://spds.sydney.edu.au/event/open-st-high-resolution-spatial-transcriptomics-in-2d-and-3d/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250922T130000
DTEND;TZID=Australia/Sydney:20250922T140000
DTSTAMP:20260405T161633
CREATED:20250905T052651Z
LAST-MODIFIED:20250922T070624Z
UID:4155-1758546000-1758549600@spds.sydney.edu.au
SUMMARY:Cell Simulation as Cell Segmentation
DESCRIPTION:Statistical Bioinformatics SeminarDr Daniel Jones\, Fred Hutchinson Cancer Institute\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSpatial transcriptomics has grown rapidly in scale and adoption\, but the quality and interpretability of the data is marred by the limited ability of prior cell segmentation methods to accurately assign transcripts to cells. Segmentation error causes systematic misidentification of cell types and profoundly confounds many spatial analyses. To help address this issue\, we developed a probabilistic segmentation algorithm\, Proseg\, which uses the spatial distribution of transcripts to determine plausibly boundaries\, dramatically reducing the degree of spurious coexpression downstream false positive results. Recent updates to the method adds support for Visium HD\, handling both imaging- and barcode-based transcriptomic data with the same unified segmentation framework. \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 Daniel Jones\n\n\n\nDaniel Jones is a staff scientist at the Fred Hutchinson Cancer Institute in the lab of Evan Newell since 2020 after receiving his PhD in Computer Science and Engineering at the University of Washington. His work focuses on probabilistic modeling and inference in spatial transcriptomics.
URL:https://spds.sydney.edu.au/event/cell-simulation-as-cell-segmentation/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250915T130000
DTEND;TZID=Australia/Sydney:20250915T140000
DTSTAMP:20260405T161633
CREATED:20250718T043847Z
LAST-MODIFIED:20250922T070523Z
UID:3943-1757941200-1757944800@spds.sydney.edu.au
SUMMARY:Bifidobacteria support optimal infant vaccine responses
DESCRIPTION:Statistical Bioinformatics SeminarDr Feargal J. Ryan\, Flinders University\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nAccumulating evidence indicates that antibiotic exposure may lead to impaired vaccine responses; however\, the mechanisms underlying this association remain poorly understood. Here we prospectively followed 191 healthy\, vaginally born\, term infants from birth to 15 months\, using a systems vaccinology approach to assess the effects of antibiotic exposure on immune responses to vaccination. Exposure to direct neonatal but not intrapartum antibiotics was associated with significantly lower antibody titres against various polysaccharides in the 13-valent pneumococcal conjugate vaccine and the Haemophilus influenzae type b polyribosylribitol phosphate and diphtheria toxoid antigens in the combined 6-in-1 Infanrix Hexa vaccine at 7 months of age. Blood from infants exposed to neonatal antibiotics had an inflammatory transcriptional profile before vaccination; in addition\, faecal metagenomics showed reduced abundance of Bifidobacterium species in these infants at the time of vaccination\, which was correlated with reduced vaccine antibody titres 6 months later. In preclinical models\, responses to the 13-valent pneumococcal conjugate vaccine were strongly dependent on an intact microbiota but could be restored in germ-free mice by administering a consortium of Bifidobacterium species or a probiotic already widely used in neonatal units. Our data suggest that microbiota-targeted interventions could mitigate the detrimental effects of early-life antibiotics on vaccine immunogenicity. \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 Feargal J. Ryan\n\n\n\nDr Feargal J. Ryan is an NHMRC Investigator and head of the Computational Multi-Omics Group at Flinders University. He specializes in systems biology\, bioinformatics and the use of multi-omics data to study molecular mechanisms of health and disease in humans. Dr Ryan has co-authored over 45 peer reviewed papers including in top journals such as Nature and Science\, which have spanned the human microbiome\, idiopathic diseases\, infection and cancer. He is also an advocate for supporting bioinformatics research in Australia and last year was elected as Vice-President of the national Australian Bioinformatics and Computational Biology Society (ABACBS). 
URL:https://spds.sydney.edu.au/event/bifidobacteria-support-optimal-infant-vaccine-responses/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250908T130000
DTEND;TZID=Australia/Sydney:20250908T140000
DTSTAMP:20260405T161633
CREATED:20250818T052708Z
LAST-MODIFIED:20250922T070418Z
UID:4113-1757336400-1757340000@spds.sydney.edu.au
SUMMARY:Large Models for Single-Cell Omics and Drug Discovery: Data\, Pretraining\, and Closed-Loop Environment
DESCRIPTION:Statistical Bioinformatics SeminarDr Haotian Cui\, University of Toronto\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nThis presentation will discuss recent advances in foundation models for single-cell omics and therapeutic discovery\, focusing on the development of scGPT\, a generative transformer model trained on over 33 million single-cell profiles. It will cover key design principles—such as generative pretraining and multi-task alignment—that enable broad applications including cell type annotation\, perturbation response prediction\, and reference mapping. The talk will also highlight emerging multimodal extensions such as scGPT-spatial and MethylGPT\, and emphasize the importance of perturbational training for modeling cellular dynamics. Finally\, it will introduce LUMI-lab\, a closed-loop self-driving platform developed by Dr. Cui and collaborators\, which integrates model-guided design\, synthesis\, and validation to accelerate mRNA delivery—demonstrating a scalable framework for virtual cell modeling and active learning in biology. \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 Haotian Cui\n\n\n\nDr. Haotian Cui is a researcher specializing in machine learning\, genomics\, and drug discovery. He recently completed his Ph.D. in Computer Science at the University of Toronto\, advised by Prof. Bo Wang. His research focuses on developing large-scale self-supervised and generative foundation models for single-cell omics and molecular biology\, integrating AI with experimental pipelines to accelerate therapeutic discovery. He is the lead author of scGPT—one of the first generative foundation models for single-cell multi-omics. He also led the development of LUMI-lab\, an autonomous AI-driven platform for mRNA therapeutics. He has published numerous papers in leading journals and conferences\, including Nature\, Nature Methods\, Nature Communications\, ACL\, EMNLP\, ICML.
URL:https://spds.sydney.edu.au/event/large-models-for-single-cell-omics-and-drug-discovery-data-pretraining-and-closed-loop-environment/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250901T130000
DTEND;TZID=Australia/Sydney:20250901T140000
DTSTAMP:20260405T161633
CREATED:20250825T055007Z
LAST-MODIFIED:20250922T065653Z
UID:4142-1756731600-1756735200@spds.sydney.edu.au
SUMMARY:A Systematic Comparison of Single-Cell Perturbation Response Prediction Models
DESCRIPTION:Statistical Bioinformatics SeminarDr Yue You\, Guangzhou National Laboratory\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nPredicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design\, yet the field lacks a rigorous\, task-spanning assessment of model behavior. We present a large-scale benchmark of 12 representative methods and 3 baselines across 25 datasets spanning diverse perturbation modalities and species\, including two new primary immune-cell drug-response resources. We evaluated three core tasks—generalization to unseen single-gene perturbations\, prediction of combinatorial interactions\, and transfer across cell types—using 24 metrics covering expression-level accuracy\, relative changes\, differential expression recovery\, and distributional similarity. \n\n\n\nAcross tasks\, performance depended strongly on perturbation effect size and evaluation perspective: expression-level agreement was highest for small-effect perturbations resembling controls\, whereas delta- and DE-based metrics improved with larger effects\, providing clearer signals. Models shared a conservative bias\, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2\, while no method consistently generalized across cell types in Task 3\, where dataset heterogeneity dominated outcomes. \n\n\n\nThis benchmark establishes current methodological limits\, clarifies when metrics diverge\, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses. \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 Yue You\n\n\n\nDr Yue You is a postdoctoral researcher at the Guangzhou National Laboratory. Her research centers on developing single-cell multi-omics algorithms and spatial-omics data analysis to decode cellular dynamics under genetic and pharmacological perturbations.
URL:https://spds.sydney.edu.au/event/a-systematic-comparison-of-single-cell-perturbation-response-prediction-models/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250825T130000
DTEND;TZID=Australia/Sydney:20250825T140000
DTSTAMP:20260405T161633
CREATED:20250806T002331Z
LAST-MODIFIED:20250922T065544Z
UID:4078-1756126800-1756130400@spds.sydney.edu.au
SUMMARY:The secret sauce - building insights from integrated atlas of human biology
DESCRIPTION:Judith and David Coffey SeminarProf Christine Wells\, University of Melbourne\n\n\n\nThis event was held in-person and online. \n\n\n\n\n\n\n\n\n\n\n\nThe human cell atlas is a global consortium seeking to build a detailed map of cell types through developmental time\, health and disease states. Molecular snapshots of individual cells can be taken at many measurement levels\, and the expectation is that integrating multiple modalities will lead to new discoveries about human cell development and behaviour. Data integration at this scale requires two main considerations – the first is statistical models to remove experimental artefacts and harmonise informative signals ; the second is curation of the metadata to identify possible technical\, experimental or even biological confounders. We often pay a lot of attention to the first challenge\, at the expense of minimizing or ignoring the second\, simply because data curation is difficult\, and often requires biological expertise that sits outside of our own groups. \n\n\n\nIn this talk\, I’ll introduce generative AI driven tools developed by my own team for stem cell curation\, as well as provide a preview of the community-driven curation platforms being lead out of the Human Cell Atlas consortium and provide some examples of how these tools are supporting high quality data integration and the creation of new cell atlases. \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 Christine Wells\n\n\n\nProfessor Christine Wells is chair of stem cell systems at the University of Melbourne. She uses computational models to understand human stem cell behaviour\, and pluripotent stem cell models to test these computational predictions. Christine graduated with a PhD from UQ in 2004\, and established her first laboratory at Griffith University in 2005. Over the past 20 years\, her team has built several human cell atlases describing immune cell subsets\, then uses these atlases to benchmark lab grown immune cells\, and have invented new protocols to make specialist immune cells in the laboratory. Christine has authored over 150 papers\, collectively cited >30\,000 times. She is a member of several international consortia including the Functional Annotation of the Mammalian genome (FANTOM)\, the Human Cell Atlas (HCA) and the equity working group for the International Society for Stem Cell Research. Christine is the architect of the Stemformatics data collaboration platform and academic lead of the Australian stem cell registry.
URL:https://spds.sydney.edu.au/event/the-secret-sauce-building-insights-from-integrated-atlas-of-human-biology/
LOCATION:Mackenzie Room\, Level 6\, Charles Perkins Centre\, University of Sydney\, Johns Hopkins Drive\, University of Sydney\, Sydney\, 2006\, Australia
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250818T130000
DTEND;TZID=Australia/Sydney:20250818T140000
DTSTAMP:20260405T161633
CREATED:20250728T015038Z
LAST-MODIFIED:20250922T065421Z
UID:4051-1755522000-1755525600@spds.sydney.edu.au
SUMMARY:The lazy way of pathological images analysis in the era of large models
DESCRIPTION:Statistical Bioinformatics SeminarDr Yimin Zheng\, CeMM Research Center for Molecular Medicine\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nHistopathological slides remain the gold standard for disease diagnosis and the study of tissue architecture and pathology\, offering an exceptionally rich yet often underutilized source of biomedical information. Unlike single-cell datasets\, pathology slides are readily available in clinical workflows and comparatively inexpensive to generate. \n\n\n\nRecent advances in foundation models have transformed our ability to extract meaningful insights from image data\, leading to a surge of high-impact studies leveraging pathology images for survival prediction\, subtyping\, spatial integration\, and more. However\, for many bioinformaticians and systems biologists\, the field of image analysis still feels inaccessible because of steep technical barriers: diverse file formats\, fragmented tools\, and a lack of intuitive frameworks for exploration and analysis. \n\n\n\nIs there a way to analyze pathology images as intuitively as we analyze single-cell data? The answer is yes! \n\n\n\nIn this talk\, I will introduce LazySlide\, an open-source platform for pathology image analysis specifically designed for users scientists already comfortable with tools like Scanpy and AnnData. LazySlide supports multiple image formats and enables interaction with large models. This talk will demonstrate how LazySlide empowers scientists to “analyze images with text” and incorporate rich histopathological information into multi-omics workflows\, without needing to become image processing experts. Whether you’re studying cancer progression\, tissue aging\, or immune landscapes\, LazySlide unlocks a new dimension of spatial and morphological context at scale. \n\n\n\nGet started with LazySlide in no time: https://github.com/rendeirolab/LazySlide \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 Yimin Zheng\n\n\n\nDr Yimin Zheng is Postdoctoral Fellow at CeMM Research Center for Molecular Medicine at Vienna\, Austria. He comes from a spatial biology background. His current research focuses on understanding cancer metastasis and aging from a multimodel pathological imaging perspective. He is also an open source contributor to multiple scientific software projects and a big-fan on visualization who has developed the composable visualization package of Marsilea.
URL:https://spds.sydney.edu.au/event/the-lazy-way-of-pathological-images-analysis-in-the-era-of-large-models/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250811T130000
DTEND;TZID=Australia/Sydney:20250811T140000
DTSTAMP:20260405T161633
CREATED:20250725T033046Z
LAST-MODIFIED:20250922T065408Z
UID:4024-1754917200-1754920800@spds.sydney.edu.au
SUMMARY:TenK10K multiome initiative: Genetic regulation of cell-type–specific chromatin accessibility shapes immune function and disease risk
DESCRIPTION:Statistical Bioinformatics SeminarDr Angli Xue\, Garvan Institute of Medical Research\n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nUnderstanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex diseases. However\, limited large-scale single-cell multi-omics data have constrained our understanding of the regulatory pathways that link variants to gene expression. Here\, we present the TenK10K multiome initiative as part of the TenK10K phase 1 projects: chromatin accessibility profiles from 3.5 million peripheral blood mononuclear cells (PBMCs) across 1\,013 donors\, generated using single-cell ATAC-seq and multiome (RNA+ATAC) sequencing\, with matched whole-genome sequencing. We characterised ~441\,000 chromatin peaks across 28 immune cell types and mapped ~243\,000 chromatin accessibility QTLs (caQTLs)\, 60% of which are cell-type-specific. Integration with TenK10K scRNA-seq (5.4 million PBMCs) identified 31\,688 candidate cis-regulatory elements colocalised with eQTLs; over half (52.5%) show evidence of causal effects mediated via chromatin accessibility. Combining caQTLs with GWAS loci for 17 diseases and 44 blood traits uncovered 10–41% more colocalised signals compared to eQTLs alone. For example\, incorporating caQTLs increased the number of candidate inflammatory bowel disease (IBD) genes in CD8 effector memory T cells from 39 to 55. We demonstrate cell-type-specific mechanisms\, such as a regulatory effect on IRGM acting through altered chromatin accessibility in CD8 effector memory T cells but not in naïve cells. Using a graphical neural network\, we link peaks to genes in unpaired multiome data with up to 80% higher accuracy than with paired data lacking QTLs\, improving gene regulatory network inference by identifying 128 additional TF–target pairs (a 22% increase). These findings provide an unprecedented single-cell map of chromatin accessibility and genetic variation in human circulating immune cells\, establishing a powerful resource for dissecting cell-type-specific regulation and advancing our understanding of genetic risk for 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 Angli Xue\n\n\n\nDr Angli Xue is an NHMRC Investigator Fellow (EL1) and a postdoctoral researcher with Prof Joseph Powell at the Garvan Institute of Medical Research. He currently leads the multiome stream of the TenK10K project — a population cohort study aiming to map 50 million human cells from 10\,000 individuals. His research focuses on leveraging large-scale multi-omics data to uncover cell type–specific genetic regulatory mechanisms and identify novel drug targets. Dr Xue earned his BSc from Zhejiang University and completed a PhD in Statistical Genetics at The University of Queensland under the supervision of Prof Jian Yang. He is the recipient of an NHMRC Investigator Grant (2025–2029) and a Ramaciotti Health Investment Grant as Chief Investigator.
URL:https://spds.sydney.edu.au/event/tenk10k-multiome-initiative-genetic-regulation-of-cell-type-specific-chromatin-accessibility-shapes-immune-function-and-disease-risk/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250804T130000
DTEND;TZID=Australia/Sydney:20250804T140000
DTSTAMP:20260405T161633
CREATED:20250721T065026Z
LAST-MODIFIED:20250725T033419Z
UID:3984-1754312400-1754316000@spds.sydney.edu.au
SUMMARY:Spatial and Functional Analysis of the Mammalian Brain
DESCRIPTION:Statistical Bioinformatics SeminarDr Rongxin Fang\, 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 mammalian brain is composed of an extraordinarily diverse cell types\, yet how these cells are spatially organized in the human brain—and how this organization differs across species or in disease—remains poorly understood. In this talk\, I will present two innovations in spatial transcriptomics aimed at addressing this gap. In the first part\, I will introduce genome-scale MERFISH\, a transcriptome imaging method capable of profiling over 4\,000 genes in situ at single-cell resolution. Applying this approach to the human cerebral cortex\, we identified more than 100 transcriptionally distinct cell populations and constructed a spatially resolved\, molecularly defined cell atlas. Comparative analysis with mouse cortex revealed conserved laminar organization\, but also species-specific differences—particularly in somatic interactions between neurons and non-neuronal cells—highlighting unique aspects of human brain organization. While MERFISH has primarily been applied to thin tissue sections (~10 µm)\, many biological questions require volumetric analysis of thicker tissue samples. In the second part of the talk\, I will present 3D MERFISH\, a method that combines MERFISH with confocal microscopy for optical sectioning and deep learning for enhanced image reconstruction. We applied this technique to mouse brain tissue up to 200 µm thick\, achieving high detection sensitivity and spatial fidelity. Together\, these genome-scale and volumetric transcriptome imaging methods expand the capabilities of spatial genomics\, enabling unprecedented insight into brain organization\, species evolution\, and disease mechanisms. \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 Rongxin Fang\n\n\n\nDr Rongxin Fang is an Assistant Professor at Stanford University in the Departments of Neurosurgery and Genetics (by courtesy) and a member of the Wu Tsai Neuroscience Institute. Rongxin received his Ph.D. in Bioinformatics and Systems Biology in the Department of Molecular and Cellular Medicine at UC San Diego\, where he was advised by Bing Ren (2015-2019). Rongxin has also received multiple fellowships and awards\, including the HHMI/Damon Runyon Postdoctoral Fellowship\, Rising Star in Health and Engineering – Johns Hopkins and Columbia University\, Next Generation Leader – Allen Institute\, Damon Runyon-Dale F. Frey Award for Breakthrough Scientists.
URL:https://spds.sydney.edu.au/event/spatial-and-functional-analysis-of-the-mammalian-brain/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250602T130000
DTEND;TZID=Australia/Sydney:20250602T140000
DTSTAMP:20260405T161633
CREATED:20250502T054819Z
LAST-MODIFIED:20250630T032454Z
UID:3635-1748869200-1748872800@spds.sydney.edu.au
SUMMARY:From benchtop to bedside: advancing multi-omics statistical methods for precision medicine
DESCRIPTION:Statistical Bioinformatics SeminarAndy Tran\, University of Sydney\n\n\n\nThis event was held in person and online. \n\n\n\n\n\n\n\n\n\n\n\nRecent advancements in biotechnology allow scientists to analyse biological samples at an unprecedented scale. This holds great promise for precision medicine by improving our understanding of diseases and developing new diagnostic tools. However\, drawing meaningful insights from this complex data demands bespoke statistical methods\, tailored to the data and context. In particular\, there is a lack of statistical tools that effectively use different data types for a clinical application. This talk introduces a workflow to compare lipid profiles and genetic profiles between humans and chimpanzees\, bringing insights into the evolutionary factors contributing to susceptibility to coronary artery disease. We also describe a framework to construct and optimise multi-platform clinical pathways\, and how this can be used to improve fairness in clinical decision-making.  \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\nAndy Tran\n\n\n\nAndy completed his undergraduate degree in pure mathematics and statistics at the University of Sydney in 2019. He then completed his MPhil in 2021\, under the supervision of A/Prof John Ormerod\, on statistical modelling of cell reprogramming. He submitted his PhD thesis in December 2024\, under the supervision of Prof Jean Yang\, which focused on translational bioinformatics for precision medicine. Andy is currently an education-focused lecturer in the School of Mathematics and Statistics at the University of Sydney.
URL:https://spds.sydney.edu.au/event/from-benchtop-to-bedside-advancing-multi-omics-statistical-methods-for-precision-medicine/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250526T130000
DTEND;TZID=Australia/Sydney:20250526T140000
DTSTAMP:20260405T161633
CREATED:20250514T042721Z
LAST-MODIFIED:20250630T032509Z
UID:3759-1748264400-1748268000@spds.sydney.edu.au
SUMMARY:Systems or sub-systems biology? Which can best help us understand the cell?
DESCRIPTION:Judith and David Coffey SeminarProf Marc Wilkins\, UNSW\n\n\n\nThis event was held in person and online. \n\n\n\n\n\n\n\n\n\n\n\nThe promise of systems biology is that the study of a cell\, via its components and their interactions\, will define and reveal emergent properties of that system. The use of large-scale ‘omics techniques is essential for this\, however many such techniques still cannot measure all relevant biomolecules and their interactions. This leaves us with an incomplete and sometimes shallow understanding. This talk will describe our research into two\, tractable sub-systems of the cell where we have sought to fully define their components\, interactions and aspects of function. \n\n\n\nFirst\, I will describe our work to fully define the ‘protein methylation network’ of a eukaryotic cell. Here\, we asked is it possible to identify all instances of a post-translational modification in a eukaryote? We also asked\, can we prove it? We then asked can we define all enzymes\, in this case methytransferases\, that are responsible for this modification – and in doing so construct a complete sub-system in the cell. We have been successful in addressing the above\, which has allowed a range of emergent properties of this network to be discovered. \n\n\n\nSecondly\, I will describe our work to understand how two regulatory subsystems in the cell – the signalling system and the histone methylation system – actually connect. In yeast\, four enzymes methylate histones and four can demethylate histones. They do so with remarkable precision and dynamism\, helping define regions of the genome to be transcribed. We asked are these enzymes phosphorylated\, and to what degree? We also asked are phosphosites associated with specific cellular responses; in effect do certain phosphorylation events on the histone methylating enzymes direct them to change chromatin in certain parts of the genome? We additionally asked can we find which kinases are responsible; thus connecting the sensing / signalling system with that of chromatin regulation? This story is less complete than that above\, but is compelling! \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 Marc Wilkins\n\n\n\nMarc is a professor of systems biology and is deputy dean (research and enterprise) in the faculty of science at UNSW. He has had a longstanding interest in understanding cells at a systems level\, and finding ways to do that. He defined the concept of the proteome\, coined the term\, and has directed entities such as the Systems Biology Initiative (SBI) and the Ramaciotti Centre for Genomics (2011-2022). He is currently the UNSW node leader in the MACSYS ARC Centre of Excellence\, which has the goal of building mathematical and computational models of whole cells. \n\n\n\nMarc has a career output of >280 publications in proteomics\, especially concerning protein post-translational modifications\, and in genomics\, transcriptomics and biological networks. In industry\, Prof. Wilkins co-founded two biotechnology companies\, both which took products to market and which were ASX-listed. Proteome Systems had a focus on proteomic technology development and its application to biodiscovery. Regeneus (now Cambium Bio) developed cell-based therapies for the treatment of inflammatory conditions.
URL:https://spds.sydney.edu.au/event/systems-or-sub-systems-biology-which-can-best-help-us-understand-the-cell/
LOCATION:Mackenzie Room\, Level 6\, Charles Perkins Centre\, University of Sydney\, Johns Hopkins Drive\, University of Sydney\, NSW\, 2006\, Australia
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250519T130000
DTEND;TZID=Australia/Sydney:20250519T140000
DTSTAMP:20260405T161633
CREATED:20250507T041753Z
LAST-MODIFIED:20250630T032526Z
UID:3688-1747659600-1747663200@spds.sydney.edu.au
SUMMARY:The 7TM family: are template-based models better than AlphaFold models?
DESCRIPTION:Judith and David Coffey SeminarProf Shoba Ranganathan\, Macquarie University\n\n\n\nThis event was held in person and online. \n\n\n\n\n\n\n\n\n\n\n\nInsect odorant and gustatory receptors (ORs/GRs) are 7-transmembrane-domain (7TM) ion channels essential for the survival of insects. ORs play a key role in many insect behaviours\, including foraging\, pollination\, social interactions\, and recognizing prey and enemies. ORs are also the target of biocontrol of insect pests and disease vectors. Ors are therefore highly specialized and bear very little sequence similarity\, even within the same species. These receptors are “upside-down” compared to G-protein coupled receptors (GPCRs)\, and function without any accessory G proteins to let calcium ions in upon ligand binding. To design or identify novel volatile attractant or repellent chemicals\, a knowledge of the 3D structure of these inverted topology 7TM receptors is essential. With the rise of AlphaFold\, currently in its third avatar\, we explored if detailed template-based modelling (TBM) using the few experimental insect OR structures available is still required\, instead of a quick AI-generated AlphaFold3 (AF3) model. Using all Ors from the genomes of two economically important Australian fruit fly pest species\, we show that 7TM receptors still need step-by-step TBM rather than AF3 models. \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 Shoba Ranganathan\n\n\n\nShoba Ranganathan is an Honorary Professor of Bioinformatics in Applied Biosciences\, Macquarie University. She was the first Chair of Bioinformatics in Australia (2004-22). She has held research and academic positions in India\, USA\, Singapore and Australia as well as a consultancy in industry. Shoba’s research addresses several key areas of bioinformatics to understand biological systems using computational approaches. Her group has achieved both experience and expertise in different aspects of computational biology\, ranging from metabolites and small molecules to biochemical networks\, pathway analysis and computational systems biology. She has authored as well as edited several books in as well as contributed several articles to the 1st edition of this Encyclopedia. She was awarded the 2023 Outstanding Contributions to the International Society for Computational Biology (ISCB) Award. She is an Honorary Senior Fellow of the Australian Society for Bioinformatics and Computational Biology since 2018\, an ISCB Fellow since 2023 and an Asia Association for Artificial Intelligence Fellow since 2024.
URL:https://spds.sydney.edu.au/event/the-7tm-family-are-template-based-models-better-than-alphafold-models/
LOCATION:Mackenzie Room\, Level 6\, Charles Perkins Centre\, University of Sydney\, Johns Hopkins Drive\, University of Sydney\, Sydney\, 2006\, Australia
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250512T130000
DTEND;TZID=Australia/Sydney:20250512T140000
DTSTAMP:20260405T161633
CREATED:20250324T010319Z
LAST-MODIFIED:20250630T032541Z
UID:3055-1747054800-1747058400@spds.sydney.edu.au
SUMMARY:BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
DESCRIPTION:Statistical Bioinformatics SeminarDr Vipul Singhal\, Head of Computational Biology\, Integrated Biosciences\, Inc\n\n\n\n\n\n\n\n\n\n\n\n\n\nA core property of solid tissue is the spatial arrangement of cell types into stereotypical spatial patterns. These cells can be investigated with spatial omics technologies to reveal both their omics features (transcriptomes\, proteomes\, etc)\, and their spatial coordinates. Because a cell’s state can be influenced by interactions with other cells\, it is informative to cluster cells using their omics signatures as well as their spatial relationships. We present BANKSY (Singhal et al.\, Nat. Genetics\, 2024)\, an algorithm with R and Python implementations that identifies both cell types and tissue domains from spatially-resolved -omics data. It does so by embedding cells in a product space of their own and neighbourhood omics features. BANKSY revealed niche-dependent cell states in the mouse brain\, and outperformed competing methods on domain segmentation and cell-typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch correction. Critically\, it is substantially faster and more scalable than existing methods\, enabling the processing of datasets with millions of cells. BANKSY comes in both Python and R implementations\, and works with major single cell frameworks like SingleCellExperiment\, Seurat\, and Scanpy. \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\n\n\n\n\nDr Vipul Singhal\n\n\n\nDr. Singhal is a computational biologist at Integrated Biosciences in Redwood City\, CA\, where he uses systems biology and machine learning to explore cellular responses to drugs and other perturbations. Previously\, he worked at the Genome Institute of Singapore\, developing algorithms to analyze spatial gene expression data in Dr. Kok Hao Chen’s lab. He earned his PhD in Bioengineering from Caltech\, focusing on tools for designing genetic circuits\, and his undergraduate degree in Electrical Engineering from Imperial College London. Outside work\, he enjoys snowboarding and climbing.
URL:https://spds.sydney.edu.au/event/banksy-unifies-cell-typing-and-tissue-domain-segmentation-for-scalable-spatial-omics-data-analysis/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250505T130000
DTEND;TZID=Australia/Sydney:20250505T140000
DTSTAMP:20260405T161633
CREATED:20250417T055319Z
LAST-MODIFIED:20250630T032301Z
UID:3569-1746450000-1746453600@spds.sydney.edu.au
SUMMARY:Vitessce framework for interactive visualization of single-cell data and its applications
DESCRIPTION:Statistical Bioinformatics SeminarMark Keller\, PhD student at Harvard Medical School\n\n\n\n\n\n\n\n\n\n\n\nWe introduce Vitessce to address the need for a scalable\, interactive\, and extensible framework that supports visualization of spatial and multimodal single-cell data. Its modular architecture\, compatibility with multiple file formats\, and support for coordinated multiple views enable researchers to integrate previously disconnected data modalities and view them with a single tool. The adoption of Vitessce by multiple data portals\, publication-associated websites\, and commercial products underscores its utility. This framework also supports multiple projects in which we have applied Vitessce for comparative\, spatial\, and epigenomic data visualization\, which will be highlighted in this presentation. \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\nMark Keller\n\n\n\nMark Keller is a PhD student in the Bioinformatics and Integrative Genomics program at Harvard Medical School\, advised by Professor Nils Gehlenborg. His research interests include developing visualization tools for single-cell data.
URL:https://spds.sydney.edu.au/event/vitessce-framework-for-interactive-visualization-of-single-cell-data-and-its-applications/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250428T130000
DTEND;TZID=Australia/Sydney:20250428T140000
DTSTAMP:20260405T161633
CREATED:20250403T231421Z
LAST-MODIFIED:20250509T062934Z
UID:3206-1745845200-1745848800@spds.sydney.edu.au
SUMMARY:Spatially resolved mapping of cells associated with human complex traits
DESCRIPTION:Liyang Song\, Westlake University\n\n\n\n\n\n\n\n\n\nDepicting spatial distributions of disease-relevant cells is crucial for understanding disease pathology. In our recent work\, we developed a method\, gsMap\, that integrates spatial transcriptomics (ST) data with genome-wide association study (GWAS) summary statistics to map cells to human complex traits\, including diseases\, in a spatially resolved manner. Using embryonic ST datasets covering 25 organs\, we benchmarked gsMap through simulation and by corroborating known trait-associated cells or regions in various organs. Applying gsMap to brain ST data\, we revealed that the spatial distribution of glutamatergic neurons (glu-neurons) associated with schizophrenia more closely resembles that for cognitive traits than that for mood traits\, such as depression. The schizophrenia-associated glu-neurons were distributed near the dorsal hippocampus\, with upregulated calcium signaling and regulation genes\, while the depression-associated glu-neurons were distributed near the deep medial prefrontal cortex\, with upregulated neuroplasticity and psychiatric drug target genes. \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\nLiyang Song\n\n\n\nLiyang Song is a fourth-year Ph.D. student in Statistical Genetics at Westlake University\, advised by Dr. Jian Yang. His research focuses on the intersection of human genetics\, statistics\, deep learning\, and spatial omics\, with applications to medicine. He is dedicated to developing efficient statistical methods and software tools that enable robust genetic and scientific discoveries by integrating genetic data with multi-omics datasets.
URL:https://spds.sydney.edu.au/event/spatially-resolved-mapping-of-cells-associated-with-human-complex-traits/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250414T130000
DTEND;TZID=Australia/Sydney:20250414T140000
DTSTAMP:20260405T161633
CREATED:20250404T031759Z
LAST-MODIFIED:20250509T063228Z
UID:3253-1744635600-1744639200@spds.sydney.edu.au
SUMMARY:RNA polymerase II at histone genes predicts outcome in human cancer
DESCRIPTION:Dr Ye Zheng\, University of Texas MD Anderson Cancer Center\n\n\n\n\n\n\n\n\n\n\n\n\n\nGenome-wide hypertranscription is common in human cancer and predicts poor prognosis. To understand how hypertranscription might drive cancer\, we applied our formalin-fixed paraffin-embedded (FFPE)–cleavage under targeted accessible chromatin method for mapping RNA polymerase II (RNAPII) genome-wide in FFPE sections. We demonstrate global RNAPII elevations in mouse gliomas and assorted human tumors in small clinical samples and discover regional elevations corresponding to de novo HER2 amplifications punctuated by likely selective sweeps. RNAPII occupancy at S-phase-dependent histone genes correlated with WHO grade in meningiomas\, accurately predicted rapid recurrence\, and corresponded to whole-arm chromosome losses. Elevated RNAPII at histone genes in meningiomas and diverse breast cancers is consistent with histone production being rate-limiting for S-phase progression and histone gene hypertranscription driving overproliferation and aneuploidy in cancer\, with general implications 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 Ye Zheng\n\n\n\nYe Zheng is a tenure-track Assistant Professor in the Bioinformatics and Computational Biology Department of the University of Texas MD Anderson Cancer Center and an NIH/NHGRI K99/R00 fellow. Dr. Zheng received her postdoctoral training at the Fred Hutchinson Cancer Center from both molecular biology and quantitative modeling perspectives mentored by Dr. Steven Henikoff. Before her postdoctoral training\, Dr. Zheng received a Ph.D. in Statistics from the University of Wisconsin-Madison under the supervision of Dr. Sündüz Keleş.  At MD Anderson Cancer Center\, Dr. Zheng leads a hybrid research group. Her quantitative research group is dedicated to the statistical modeling and computational pipeline development using bulk and single-cell transcriptomics\, proteomics\, epigenomics\, and 3D genomics data to address biological and clinical challenges. Her wet lab specializes in the epigenomic profiling of the Formalin-Fixed\, Paraffin-Embedded (FFPE) samples.
URL:https://spds.sydney.edu.au/event/rna-polymerase-ii-at-histone-genes-predicts-outcome-in-human-cancer/
CATEGORIES:Videos
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250407T130000
DTEND;TZID=Australia/Sydney:20250407T140000
DTSTAMP:20260405T161633
CREATED:20250110T033554Z
LAST-MODIFIED:20250509T062211Z
UID:1324-1744030800-1744034400@spds.sydney.edu.au
SUMMARY:Toward a cell-type-specific understanding of complex diseases
DESCRIPTION:Statistical Bioinformatics SeminarDr Boxiang Liu\, National University of Singapore\n\n\n\n\n\n\n\n\n\n\n\n\n\nHigh-throughput genotyping and sequencing have led to the discovery of thousands of disease-associated variants. Because most of these variants lie in non-coding regions\, their functional mechanisms remain unclear. To identify genetic effects underlying complex diseases\, it has become increasingly important to investigate the proper cell types and contexts. We demonstrate the power of cell-type-specific assays for three complex diseases.  \n\n\n\nCoronary artery disease (CAD) is the leading cause of death globally. Approximately 40 – 60% of CAD severity can be attributed to genetic factors. GWAS meta-analyses have uncovered more than 100 significant loci\, but most are difficult to interpret because they reside in non-coding regions. We found that coronary artery smooth muscle-specific genetic regulatory mechanisms are highly enriched in CAD GWAS signals. By jointly analyzing eQTL and GWAS datasets\, we identified five risk genes. TCF21 and SMAD3 were subsequently validated by single-cell analysis in atherosclerotic mouse models. Age-related macular degeneration is one of the leading causes of blindness in elderlies. It has been estimated that genetic factors explain 45% – 70% of the variation in the severity of age-related macular degeneration. Retinal pigment epithelium (RPE) is vital in ocular development but is underrepresented in genetic regulation studies.  \n\n\n\nBy jointly analyzing RPE eQTL and AMD GWAS\, we identified several risk genes including RDH5. In particular\, we found that the eQTL regulatory SNP also regulates splicing. Experimental validation confirms that the minor allele leads to aberrant splicing and subsequently RNA non-sense-mediated decay. This result revealed the genetic mechanism of RDH5 regulation and confirmed RDH5 as a risk gene for age-related macular degeneration\, making it a potential target for drug development. Autoimmune diseases are a group of illnesses that are individually rare but collectively affect 5% of the population. Leveraging ~1M PBMC single cells from the Asian Immune Diversity Atlas (AIDA)\, we showcase our recent results on leveraging single-cell sQTLs to disentangle autoimmune 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\nBoxiang Liu obtained BA degree in Biophysics from Illinois Wesleyan University\, a MS degree in Statistics and a PhD degree in Bioinformatics from Stanford University. He was a research leader at Baidu Research USA and joined the National University of Singapore as an Assistant Professor in 2022. His research group specializes in genetic regulation of molecular traits (QTLs) and single-cell multi-omics. He is the winner of President’s Award in Natural Sciences and Mathematics\, Stanford University CEHG fellowship\, Charles B. Carrington Memorial Award\, and the Chinese Government Outstanding Overseas Ph.D. Students. His research group focuses on using computational and statistical tools to understand the genetics of complex human diseases\, with the long-term goal of validating known and identifying novel drug targets.
URL:https://spds.sydney.edu.au/event/toward-a-cell-type-specific-understanding-of-complex-diseases/
CATEGORIES:Videos
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250331T130000
DTEND;TZID=Australia/Sydney:20250331T140000
DTSTAMP:20260405T161633
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250324T130000
DTEND;TZID=Australia/Sydney:20250324T140000
DTSTAMP:20260405T161633
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250317T130000
DTEND;TZID=Australia/Sydney:20250317T140000
DTSTAMP:20260405T161633
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250310T130000
DTEND;TZID=Australia/Sydney:20250310T140000
DTSTAMP:20260405T161633
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250303T130000
DTEND;TZID=Australia/Sydney:20250303T140000
DTSTAMP:20260405T161633
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250224T130000
DTEND;TZID=Australia/Sydney:20250224T140000
DTSTAMP:20260405T161633
CREATED:20241126T005558Z
LAST-MODIFIED:20250509T062253Z
UID:1227-1740402000-1740405600@spds.sydney.edu.au
SUMMARY:Computational Approaches in Functional Genomics: Understanding Gene Regulation and Development
DESCRIPTION:Statistical Bioinformatics SeminarDr Luca Pinello\, Harvard Medical School\n\n\n\n\n\n\n\n\n\n\n\nIn this talk\, Dr Luca Pinello will present an overview of his lab’s recent work at the intersection of CRISPR genome editing\, single-cell omics assays\, and generative AI to elucidate gene regulation. His lab’s research focuses on developing computational methods to investigate genotype-phenotype relationships at high resolution\, infer dynamic gene regulatory networks\, and integrate multi-omics data. By leveraging state-of-the-art technologies and advanced computational approaches\, his lab aims to uncover the complexities of cellular heterogeneity\, identify key regulatory elements and transcription factors\, and understand the interplay between chromatin structure and function in normal development and disease. \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\n\n\n\n\nDr Luca Pinello\n\n\n\nLuca Pinello is a computational biologist and leader in developing computational methods for functional genomics\, genome editing and single cell technologies. He holds a Ph.D. in Mathematics and Computer Science from University of Palermo\, Italy. He is currently an Associate Pathologist at Massachusetts General Hospital (MGH) and an Associate Professor of Pathology at Harvard Medical School. He is also part of the MGH Center for Cancer Research and an Associate Member of the BROAD Institute of MIT and Harvard. He has developed several foundational computational tools in the field of genome editing for the design (CRISPRme\, CRISPRitz\, PrimeDesign)\, quantification (CRISPResso 1 and 2)\, and analyses of coding and non-coding tiling screens (CRISPRO\, CRISPR-SURF). He was awarded one of the first NIH R35 Genomic Innovator Awards\, a prestigious grant supporting highly innovative researchers working on important problems in genomics. 
URL:https://spds.sydney.edu.au/event/statistical-bioinformatics-seminar-series/
CATEGORIES:Videos
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241216T130000
DTEND;TZID=Australia/Sydney:20241216T140000
DTSTAMP:20260405T161633
CREATED:20241128T003907Z
LAST-MODIFIED:20250110T033323Z
UID:1244-1734354000-1734357600@spds.sydney.edu.au
SUMMARY:Incorporating experimental medicine into single-cell multi-omics
DESCRIPTION:Special Statistical Bioinformatics Seminar\n\n\n\n\n\nDr Jacqueline Siu\, Kennedy Institute of Rheumatology\, University of Oxford \n\n\n\n \n\n\n\n\n\nAbout the seminar: Coordinated global efforts to create a comprehensive map of the human body using single-cell technologies has opened up avenues in computational biology and AI. However\, there remains a pressing need to increase the data diversity from the ancestral diversity of participants to increasing the types of immune perturbations captured. This talk will focus on incorporating single-cell multi-omics to different experimental medicine approaches–such as deceased organ donors\, vaccine and xenogeneic-antigen challenge trials–in order to understand healthy immune responses in lymphoid organs. \n\n\n\nAbout the speaker: Jacqueline Siu received her PhD at the University of Cambridge where she combined bioinformatics and experimental medicine to understand the function and differentiation of homeostatic human B cells in matched secondary lymphoid organs. To extend her interest in human tissue immunity especially after perturbation\, Jacqueline joined the labs of Professor Mark Coles/ Professor Calliope Dendrou as a postdoctoral fellow\, where she explored the influenza vaccine response of human axillary lymph nodes. In 2023\, Jacqueline received the Wellcome Trust Early Career award to continue her interest in integrating human challenge models and single-cell genomics to understand early responding human B cells and their impact on long-lived antibodies.
URL:https://spds.sydney.edu.au/event/statistical-bioinformatics-seminar-judith-david-coffey-invited-speaker-special-seminar/
LOCATION:Mackenzie Room\, Level 6\, Charles Perkins Centre\, University of Sydney\, Johns Hopkins Drive\, University of Sydney\, NSW\, 2006\, Australia
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241213T140000
DTEND;TZID=Australia/Sydney:20241213T150000
DTSTAMP:20260405T161633
CREATED:20241127T231618Z
LAST-MODIFIED:20250509T064329Z
UID:1236-1734098400-1734102000@spds.sydney.edu.au
SUMMARY:Veridical data science and alignment in medical AI
DESCRIPTION:Professor Bin Yu\, Statistics\, EECS\, Center for Computational Biology\, Simons Institute for the Theory of Computing\, UC Berkeley \n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\nAbout the seminar\n\n\n\nAlignment and trust are crucial for the successful integration of AI in healthcare including digital twin projects\, a field involving diverse stakeholders such as medical personnel\, patients\, administrators\, public health officials\, and taxpayers\, all of whom influence how these concepts are defined. This talk presents a series of collaborative medical case studies where AI algorithms progressively become\, from transparency to more opaque thus with increasing difficulty of alignment assessment. These range from tree-based methods for trauma diagnosis\, to LLM-based emergency department co-pilot\, and mechanistic circuits for structured data extraction from pathology reports. They are guided by Veridical Data Science (VDS) principles—Predictability\, Computability\, and Stability (PCS)—for the goal of building trust and interpretability\, enabling doctors to assess alignment. The talk concludes with a discussion on applying VDS to digital twins and medical foundation models and next steps for evaluating AI algorithm alignment in healthcare. \n\n\n\n\n\n\n\n\n\nAbout Bin Yu\n\n\n\nBin Yu is CDSS Chancellor’s Distinguished Professor in Statistics\, EECS\, and Computational Biology\, and Scientific Advisor at the Simons Institute for the Theory of Computing\,  all at UC Berkeley. Her research focuses on the practice and theory of statistical machine learning\, veridical data science\, and solving interdisciplinary data problems in neuroscience\, genomics\, and precision medicine. She and her team have developed algorithms such as iterative random forests (iRF)\, stability-driven NMF\, and adaptive wavelet distillation (AWD) from deep learning models. She is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She was a Guggenheim Fellow\, IMS President\, and delivered the IMS Rietz and Wald Lectures and Distinguished Achievement Award and Lecture (formerly Fisher Lecture) of COPSS. She holds an Honorary Doctorate from The University of Lausanne
URL:https://spds.sydney.edu.au/event/veridical-data-science-and-alignment-in-medical-ai/
LOCATION:Lecture Theatre 173\, Carslaw Building (F07.01.173)
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DTSTART;TZID=Australia/Sydney:20241125T130000
DTEND;TZID=Australia/Sydney:20241125T140000
DTSTAMP:20260405T161633
CREATED:20241104T062549Z
LAST-MODIFIED:20250110T033504Z
UID:749-1732539600-1732543200@spds.sydney.edu.au
SUMMARY:Indigenous Australian genomes show deep population structure and abundant novel variation
DESCRIPTION:Special Judith and David Coffey Seminar\n\n\n\n\n\n \n\n\n\nProf Stephen Leslie\, The University of MelbourneAbstract: Without the inclusion of diverse genetic ancestries in reference resources\, inequity in research and clinical practice risks being entrenched. A handful ethnicities have been the focus of genomic research to date\, and Indigenous Australians are virtually absent from global reference panels and genomic analyses. The National Centre for Indigenous Genomics (NCIG) has collected genomic data from four Indigenous Australian communities from distinct regions of Australia.  This is the largest sample of Indigenous Australian whole genomes to date\, made possible by careful engagement and consultation with the communities\, setting new standards for best practice in Indigenous research. In this talk I will show analyses of the genomes of 159 individuals from these four communities\, investigating patterns of genetic variation and diversity within this dataset\, as well as the causes and consequences of these patterns.  \n\n\n\nWe discover substantial uncharacterised genetic variation in Indigenous Australians when compared to worldwide populations\, with implications for the utility of global reference panels and databases for conducting research within these communities.  We show the variation is shaped by exceptionally strong population structure across Australia and explore its features and the factors that have caused such structure. Our analysis emphasizes the distinctiveness of Indigenous Australians in a worldwide context with consequences for the design and implementation of genomic research and clinical practice in Indigenous communities.   \n\n\n\nAbout the speaker: Prof. Stephen Leslie is a statistician working in the field of mathematical genetics.  He is currently Professor of Statistical Genomics in the School of Mathematics and Statistics\, and Melbourne Integrative Genomics\, at the University of Melbourne.  Stephen has made key contributions to detecting\, understanding\, and controlling for population differences in genetic data\, including his landmark papers on fine-scale population genetic differentiation in the UK\, and more recently for Indigenous Australians.  He also developed the first methods for typing HLA and KIR alleles (major immune-associated gene families) from SNP data\, making these important variants available for large datasets world-wide\, including as part of the main data release for the UK Biobank.  Stephen has won several major awards for his work including the Woodward Medal of the University of Melbourne\, and the Moran Medal of the Australian Academy of Science. \n\n\n\nStephen’s research focuses on understanding the association of population genetic variation and history/demography; uncovering the relationship of immune-associated loci and autoimmune disease; and performing statistically rigorous analyses of the relationship of genetic variants to disease. 
URL:https://spds.sydney.edu.au/event/special-judith-and-david-coffey-seminar-prof-stephen-leslie/
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