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X-WR-CALNAME:Sydney Precision Data Science Centre
X-ORIGINAL-URL:https://spds.sydney.edu.au
X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260330T130000
DTEND;TZID=Australia/Sydney:20260330T140000
DTSTAMP:20260417T221311
CREATED:20260206T033832Z
LAST-MODIFIED:20260410T051110Z
UID:4690-1774875600-1774879200@spds.sydney.edu.au
SUMMARY:mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Pan Liu\, Fred Hutchinson Cancer Center \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\nIn single-cell data analysis\, addressing sparsity often involves aggregating the profiles of homogeneous single cells into metacells. However\, existing metacell partitioning methods lack checks on the homogeneity assumption and may aggregate heterogeneous single cells\, potentially biasing downstream analysis and leading to spurious discoveries. To fill this gap\, we introduce mcRigor\, a statistical method to detect dubious metacells\, which are composed of heterogeneous single cells\, and optimize the hyperparameter of a metacell partitioning method. The core of mcRigor is a feature-correlation-based statistic that measures the heterogeneity of a metacell\, with its null distribution derived from a double permutation scheme. As an optimizer for existing metacell partitioning methods\, mcRigor has been shown to improve the reliability of discoveries in single-cell RNA-seq and multiome (RNA+ATAC) data analyses\, such as uncovering differential gene co-expression modules\, enhancer-gene associations\, and gene temporal expression. Moreover\, mcRigor enables benchmarking and selection of the most suitable metacell partitioning method with optimized hyperparameters tailored to specific datasets\, ensuring reliable downstream analysis.  \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 Pan Liu\n\n\n\nDr Pan Liu is a postdoctoral researcher in the Department of Biostatistics at the Fred Hutchinson Cancer Center\, working with Professor Jingyi Jessica Li. The study she will present today was primarily conducted during her postdoctoral training in the Department of Statistics and Data Science at UCLA\, after which she moved to Fred Hutch with Professor Li. Pan earned her Ph.D. in Statistics from the National University of Singapore under the supervision of Professor Jialiang Li. Before that\, she received her B.S. degree in Mathematics from the Chu Kochen Honors College of Zhejiang University. Her research focuses on developing rigorous and reliable statistical methods for single-cell and spatial transcriptomic data analysis\, as well as tools for subgroup identification in biomedical research.Find out more on LinkedInlinkedin.com/in/pan-liu-bb17b3265
URL:https://spds.sydney.edu.au/event/mcrigor-a-statistical-method-to-enhance-the-rigor-of-metacell-partitioning-in-single-cell-data-analysis/
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/02/Complex-systems-1-edited-scaled.jpeg
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260413T130000
DTEND;TZID=Australia/Sydney:20260413T140000
DTSTAMP:20260417T221311
CREATED:20260327T034950Z
LAST-MODIFIED:20260417T040907Z
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 was an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\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:20260417T221311
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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DTSTART;TZID=Australia/Sydney:20260504T130000
DTEND;TZID=Australia/Sydney:20260504T140000
DTSTAMP:20260417T221311
CREATED:20260417T014417Z
LAST-MODIFIED:20260417T014701Z
UID:4861-1777899600-1777903200@spds.sydney.edu.au
SUMMARY:PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Weize Xu\, 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 convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However\, existing biological agent systems\, building upon single-agent architectures\, are narrowly specialized or overly general\, limiting applications to routine analyses. We introduce PantheonOS (https://PantheonOS.stanford.edu)\, an evolvable\, privacy-preserving multi-agent framework designed to reconcile generality with domain specificity. Critically\, PantheonOS enables agentic code evolution\, allowing evolving state-of-the-art batch correction and our reinforcement-learning augmented gene panel selection algorithms to achieve super-human performance. PantheonOS drives biological discoveries across systems: uncovering asymmetric paracrine Cer1–Nodal inhibition in proximal–distal axis formation of novel early mouse embryo 3D data; integrating human fetal heart multi-omics with whole-heart data to reveal molecular programs underpin heart diseases; and adaptively selecting virtual cell models to predict cardiac regulatory and perturbation effects. Together\, PantheonOS points towards a future where scientific discoveries are increasingly driven by self-evolving AI systems across biology and beyond. \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 Weize Xu\n\n\n\nDr. Weize Xu is a postdoctoral researcher in Dr. Xiaojie Qiu’s laboratory\, where he focuses on advancing computational biology and genomics research. He earned his Ph.D. in Dr. Gang Cao’s lab\, where he made significant contributions to the development of computational methods and pipelines for spatial transcriptomics (MiP-Seq) and single-cell Hi-C (sciDLO Hi-C). His work during this time centered on enhancing data analysis frameworks\, providing more precise insights into complex biological systems.Find out more on X:https://x.com/Nanguage
URL:https://spds.sydney.edu.au/event/pantheonos-an-evolvable-multi-agent-framework-for-automatic-genomics-discovery/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260601T130000
DTEND;TZID=Australia/Sydney:20260601T140000
DTSTAMP:20260417T221311
CREATED:20260417T015751Z
LAST-MODIFIED:20260417T015752Z
UID:4869-1780318800-1780322400@spds.sydney.edu.au
SUMMARY:Through the Unlabeled Lens of Spatial Multi-Omics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Anthony A. Fung\, Yale University \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\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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