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DTSTART;TZID=Australia/Sydney:20260601T130000
DTEND;TZID=Australia/Sydney:20260601T140000
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SUMMARY:Through the Unlabeled Lens of Spatial Multi-Omics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Anthony A. Fung\, Yale University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSometimes it matters less what you look at\, and more what you see. Common practices in clinical pathology often involve multiple histological stains on serial sections of tissue biopsy to obtain the highest diagnostic power\, but this requires expertise\, reagent costs\, consumes tissue\, risks deformation\, and complicates co-registration\, potentially missing rare microstructures. Now there is a major push for spatial multi-omics integration\, but even adjacent tissue sections captured with different modalities decrease performance. Today’s seminar introduces a non-destructive label-free optical platform combining SRS\, SHG\, and TPF enables high-resolution molecular imaging to unravel the lipidomic\, metabolic\, and morphometric landscape of kidney disease\, and how these data types can augment your modalities. \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 Anthony A. Fung\n\n\n\nDr Anthony A. Fung is a T32 postdoctoral fellow in Professor Rong Fan’s group at Yale University. He received his PhD in Bioengineering at University of California San Diego from Professor Lingyan Shi’s group. Anthony has received several awards in the quantitative spatial biology field and is a collaborating investigator in both HuBMAP and SenNet consortia. His current work centers on the development and application of spatial multi-omics technologies in aging and immune senescence.Find out more on LinkedIn:https://www.linkedin.com/in/anthony-fung/
URL:https://spds.sydney.edu.au/event/through-the-unlabeled-lens-of-spatial-multi-omics/
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DTSTART;TZID=Australia/Sydney:20260615T130000
DTEND;TZID=Australia/Sydney:20260615T140000
DTSTAMP:20260619T105424Z
CREATED:20260601T020224Z
LAST-MODIFIED:20260619T105424Z
UID:5173-1781528400-1781532000@spds.sydney.edu.au
SUMMARY:Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Long Yuan\, Johns Hopkins University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nIn imaging-based spatial transcriptomics\, transcript-to-cell assignment shapes downstream biological interpretation\, including cell typing\, ligand–receptor inference\, and niche characterization. However\, two-dimensional segmentation of volumetric tissue often yields mixed cellular profiles\, while cells without detected nuclei may be missed entirely\, affecting downstream analyses. We present TRACER\, a framework that refines cellular representations in imaging-based spatial transcriptomics by leveraging gene–gene coherence and spatial co-localization of transcripts observed directly in the data\, without requiring external annotations or reference atlases. TRACER resolves mixed cellular profiles and reconstructs partial cells whose nuclei are not detected\, enabling more complete representation of cells within tissue sections. We also introduce coherence-based metrics that quantify transcriptional purity and conflict\, enabling platform-agnostic benchmarking of segmentation quality. Across diverse platforms\, tissues\, and segmentation methodologies\, TRACER consistently improves the coherence of cellular profiles and the quality of downstream analyses. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLong Yuan\n\n\n\nLong Yuan is a PhD candidate in Immunology and an M.S.E. candidate in Computer Science at Johns Hopkins University. His research focuses on developing scalable machine learning and statistical methods for spatial and single-cell omics\, with applications in cancer biology and immune-metabolic diseases. His work spans spatial multi-omics\, graph-based learning\, and multimodal data integration. As a member of the Break Through Cancer GBM and Data Science TeamLab\, he develops computational approaches for integrating and analyzing large-scale spatial omics datasets.Find out more on LinkedIn and X:https://www.linkedin.com/in/long-yuan-3a8b953aa/@Long_et_al
URL:https://spds.sydney.edu.au/event/reconstructing-biologically-coherent-cellular-profiles-from-imaging-based-spatial-transcriptomics/
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DTSTART;TZID=Australia/Sydney:20260622T130000
DTEND;TZID=Australia/Sydney:20260622T140000
DTSTAMP:20260629T042022Z
CREATED:20260616T235053Z
LAST-MODIFIED:20260629T042022Z
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SUMMARY:Statistical Brain Network Analysis: Recent Developments and Future Directions
DESCRIPTION:Judith and David Coffey SeminarSpeaker: Prof Sean L. Simpson\, Wake Forest University \n\n\n\nThis was a hybrid event. In-person in the Mackenzie Seminar Room\, Level 6\, Charles Perkins Centre and online via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nThe recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless\, many statistical challenges remain to be able to fully realize the promise of this field. Here we touch on a few of these challenges\, briefly survey three complementary statistical frameworks that we have developed to attempt to address a subset of these needs—a mixed modeling framework\, a distance regression framework\, and a hidden semi-Markov modeling framework—and discuss potential future avenues of research. \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 Sean L. Simpson\n\n\n\nProf Sean L. Simpson is a biostatistician in the Department of Biostatistics and Data Science\, with joint appointments in Biomedical Engineering and Neuroscience\, and an Affiliate appointment with the Maya Angelou Center for Healthy Communities (MARCH) at Wake Forest University School of Medicine. His main research focus has been on the development of novel fusions of statistical tools with network science methods for the analysis of whole-brain network data. Studying the brain as a whole and statistically accounting for the inherent complexity in the way various regions of the brain interact will engender a more biologically meaningful approach to understanding the root causes of a number of brain diseases and disorders. 
URL:https://spds.sydney.edu.au/event/statistical-brain-network-analysis-recent-developments-and-future-directions/
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