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DTSTART;TZID=Australia/Sydney:20260413T130000
DTEND;TZID=Australia/Sydney:20260413T140000
DTSTAMP:20260409T031850
CREATED:20260327T034950Z
LAST-MODIFIED:20260327T041638Z
UID:4838-1776085200-1776088800@spds.sydney.edu.au
SUMMARY:Characterizing cell-type spatial relationships across length scales in spatially resolved omics data
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Rafael dos Santos Peixoto\, Johns Hopkins University \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nSpatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships\, particularly across different length scales\, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships\, we present CRAWDAD\, Cell-type Relationship Analysis Workflow Done Across Distances\, as an open-source R package. To demonstrate the utility of such multi-scale characterization\, recapitulate expected cell-type spatial relationships\, and evaluate against other cell-type spatial analyses\, we apply CRAWDAD to various simulated and real SRO datasets across diverse tissues and SRO technologies. We further demonstrate how such multi-scale characterization\, enabled by CRAWDAD\, can be used to compare cell-type spatial relationships across multiple samples. Finally\, we apply CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general\, we anticipate that such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification\, characterization\, and comparison of cell-type spatial relationships across axes of interest. \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\nRafael dos Santos Peixoto\n\n\n\nRafael is a PhD candidate in Biomedical Engineering at Johns Hopkins University. Under the supervision of Jean Fan\, he develops software to analyze spatial omics data. His first project was CRAWDAD\, an R package to analyze cell-type spatial relationships. Now\, he is investigating the molecular differences in acute kidney injury. Outside of the lab\, he enjoys playing and watching sports. He is from Brazil\, and hopes they will win this World Cup!Find out more on X and LinkedIn:https://x.com/rdsantospeixotohttps://www.linkedin.com/in/rafaeldossantospeixoto/
URL:https://spds.sydney.edu.au/event/characterizing-cell-type-spatial-relationships-across-length-scales-in-spatially-resolved-omics-data/
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DTSTART;TZID=Australia/Sydney:20260420T130000
DTEND;TZID=Australia/Sydney:20260420T140000
DTSTAMP:20260409T031850
CREATED:20260330T021527Z
LAST-MODIFIED:20260331T033542Z
UID:4848-1776690000-1776693600@spds.sydney.edu.au
SUMMARY:Integrative transcriptome-based drug repurposing in tuberculosis
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Kewalin Samart\, University of Colorado \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\nTuberculosis (TB) remains the leading cause of death from infectious disease\, with rising antibiotic resistance highlighting the need for host-directed therapeutics (HDTs). Transcriptome-based connectivity mapping offers a promising strategy by identifying drugs that reverse disease gene expression signatures\, but current approaches are limited by reliance on single datasets\, platforms\, or scoring methods. \n\n\n\nHere\, we present a unified computational framework that systematically integrates heterogeneous transcriptomic data and multiple connectivity methods for robust drug prioritization. Our approach combines 28 TB gene expression signatures spanning microarray and RNAseq platforms\, diverse cell types\, and infection conditions\, and applies multi-method connectivity scoring to identify consistent disease-drug reversal signals. By aggregating signals across datasets and methods\, the framework captures dominant TB signatures while mitigating platform and biological variability. Using this integrative strategy\, we prioritized 64 FDA-approved drugs as candidate HDTs\, including previously reported host-directed therapeutic candidates such as statins and tamoxifen. Downstream pathway and network analyses further revealed enrichment in TB-relevant mechanisms and identified key bridging genes (e.g.\, IL-8\, CXCR2) as potential therapeutic targets. \n\n\n\nThis work establishes transcriptome-based connectivity mapping as a viable approach for systematic HDT discovery in bacterial infections and provides a robust computational framework applicable to other infectious diseases. Our findings offer immediate opportunities for experimental validation of prioritized drug candidates and mechanistic investigation of identified druggable targets in TB pathogenesis. \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\nKewalin Samart\n\n\n\nKewalin Samart is a PhD candidate in Computational Bioscience at the University of Colorado Anschutz Medical Campus. She earned her B.Sc. in Computational Mathematics from Michigan State University. Her research focuses on developing and applying computational methods to uncover host response mechanisms and identify novel host-directed therapeutic strategies for infectious diseases.Find out more on X and LinkedIn:https://x.com/KewalinSamarthttps://www.linkedin.com/in/kewalinsamart
URL:https://spds.sydney.edu.au/event/integrative-transcriptome-based-drug-repurposing-in-tuberculosis/
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