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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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DTSTART;TZID=Australia/Sydney:20260316T130000
DTEND;TZID=Australia/Sydney:20260316T140000
DTSTAMP:20260409T082958
CREATED:20251013T015305Z
LAST-MODIFIED:20260206T035342Z
UID:4226-1773666000-1773669600@spds.sydney.edu.au
SUMMARY:A multi-omics spatial framework for host-microbiome dissection within the intestinal tissue microenvironment
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Bokai Zhu\, MIT\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 intricate interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here\, we introduce Microbiome Cartography (MicroCart)\, a framework for simultaneous in situ probing of host and microbiome across multiple spatial modalities. We demonstrate MicroCart by investigating gut host and microbiome changes in a murine colitis model\, using spatial proteomics\, transcriptomics\, and glycomics. Our findings reveal a global but systematic transformation in tissue immune responses\, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations\, bacterial population shifts\, localized inflammatory responses\, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multi-omics. \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\nBokai Zhu\n\n\n\nI am currently a postdoctoral researcher at MIT under the supervision of Professor Alex Shalek. I received my PhD from the Department of Microbiology and Immunology at Stanford University\, where I trained with Professor Garry Nolan. My research focuses on integrating experimental and computational approaches in systems immunology to study tissue biology in disease contexts. On the computational side\, I develop algorithms for cross-modality single-cell data integration; on the experimental side\, I develop multiplex imaging assays to detect viruses and microbiota within complex host–tissue environments. This interdisciplinary training has equipped me with complementary skill sets spanning molecular method development\, animal models\, clinically relevant sample analysis\, and rigorous statistical and mathematical modeling. Working at the interface of computation and experimentation has enabled me to initiate and lead collaborative projects across institutions including MIT\, Stanford\, Harvard\, and Yale\, collaborating closely with both early-career and established investigators.Connect with Bokai @ZhuBokaihttps://bokaizhu.github.io/
URL:https://spds.sydney.edu.au/event/advancing-polygenic-risk-prediction-integrating-information-across-populations-and-health-records/
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:20260323T130000
DTEND;TZID=Australia/Sydney:20260323T140000
DTSTAMP:20260409T082958
CREATED:20260311T050402Z
LAST-MODIFIED:20260311T050403Z
UID:4821-1774270800-1774274400@spds.sydney.edu.au
SUMMARY:Polygenic Prediction of Treatment Efficacy with Causal Transfer Learning
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Jiacheng Miao\, 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\nTherapeutic interventions often exhibit heterogeneous treatment effects (HTE) across individuals. A central goal of precision medicine is to enable personalized treatment recommendations based on patients’ measurable characteristics. However\, detecting HTE remains challenging due to limited sample size in randomized controlled trials (RCTs)\, often-missing baseline information\, and suboptimal statistical methods with limited power. In this talk\, I will introduce a principled statistical framework named M-Learner to identify genetically-driven HTE. This approach leverages genetic variation involved in diverse biological pathways influencing drug response\, integrates insights from two decades of complex trait genetics\, and employs causal transfer learning applicable to both individual-level data and summary statistics. Applying M-Learner to multiple RCTs\, we found low bone mineral density as a key determinant of secukinumab efficacy in ankylosing spondylitis\, and identified smoker subpopulations adversely affected by a bronchodilator treatment. Our findings demonstrate the utility of genetic variation in HTE inference and make important advances toward the promise of precision medicine. \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\nJiacheng Miao\n\n\n\nJiacheng Miao is a postdoctoral fellow at Stanford University working with Jonathan Pritchard and James Zou. He is building Agentic AI systems that augment and accelerate researchFind out more on Xhttps://x.com/Jiacheng_Miao
URL:https://spds.sydney.edu.au/event/polygenic-prediction-of-treatment-efficacy-with-causal-transfer-learning/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260330T130000
DTEND;TZID=Australia/Sydney:20260330T140000
DTSTAMP:20260409T082958
CREATED:20260206T033832Z
LAST-MODIFIED:20260212T225958Z
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: 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\nPan Liu\n\n\n\nPan 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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DTSTART;TZID=Australia/Sydney:20260413T130000
DTEND;TZID=Australia/Sydney:20260413T140000
DTSTAMP:20260409T082958
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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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260420T130000
DTEND;TZID=Australia/Sydney:20260420T140000
DTSTAMP:20260409T082958
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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