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DTSTART;TZID=Australia/Sydney:20240422T130000
DTEND;TZID=Australia/Sydney:20240422T140000
DTSTAMP:20260413T095500
CREATED:20241025T031405Z
LAST-MODIFIED:20241108T045121Z
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SUMMARY:Systematic comparison of sequencing-based spatial transcriptomic methods with cadasSTre and SpatialBen
DESCRIPTION:Statistical Bioinformatics Seminar: Judith and David Coffey Speaker\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker: Prof Matthew Ritchie (WEHI) \n\n\n\nAbstract: Sequencing-based Spatial Transcriptomics (sST) allows gene expression to be measured within complex tissue contexts. Although a wide array of sST technologies are currently available to researchers\, efforts to comprehensively benchmark different platforms are currently lacking. The inherent variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. To address this\, we established a collection of reference tissues and regions characterized by well-defined histological architecture and other biological ground truth and used them to generate the cadasSTre and SpatialBench datasets that compare 11 sST methods. We highlight molecular diffusion as a variable parameter across different methods and tissues\, significantly impacting the effective resolution. Furthermore\, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data\, including an enhanced ability to capture patterned rare cell states along with specific markers\, albeit being influenced by multiple factors including sequencing depth and resolution. For the 10X Visium platform\, we benchmarked the performance of different sample handling approaches after preprocessing\, explored spatially variable gene detection and the ability of clustering and cell deconvolution to identify expected cell types and tissue regions. Multi-sample differential expression analysis was able to recover known gene signatures related to biological sex or gene knockout. Our datasets and analyses serve as a practical guide for sST users and will be useful in future benchmarking studies. \n\n\n\nAbout the speaker: Professor Matt Ritchie has been at lab head at the WEHI for the past 11 years. His team develops analysis methods and open-source software tailored to new applications of genomic technology in biomedical research. In the single-cell and spatial biology field\, this work includes tools for data preprocessing (scPipe)\, benchmarking at scale (CellBench) and new protocols and analysis methods (FLAMES) for applying long-read sequencing to single-cell research. His most recent research is on developing benchmarking resources for sequencing-based spatial transcriptomics technologies (cadasSTre and SpatialBench). Matt completed his PhD on microarray data analysis at WEHI in 2005 under the supervision of Professor Gordon Smyth\, which was followed by a period of post-doctoral research at the EBI (Hinxton\, UK) and University of Cambridge before returning to WEHI as a Senior Research Officer in 2008. He is a keen advocate of open-source software\, having served on both the Technical Advisory Board and Community Advisory Board of the Bioconductor project.
URL:https://spds.sydney.edu.au/event/systematic-comparison-of-sequencing-based-spatial-transcriptomic-methods-with-cadasstre-and-spatialben/
LOCATION:Charles Perkins Centre\, University of Sydney\, Johns Hopkins Dr\, Sydney\, NSW\, 2050\, Australia
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2024/10/nutriomics-scaled.jpg
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DTSTART;TZID=Australia/Sydney:20240415T130000
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DTSTAMP:20260413T095500
CREATED:20241025T031405Z
LAST-MODIFIED:20241025T031405Z
UID:347-1713186000-1713189600@spds.sydney.edu.au
SUMMARY:High dimensional tensor methods for multi-modal single cell genomics data
DESCRIPTION:Statistical Bioinformatics Seminar\n\n\n\nSpeaker: Kwangmoon Park (University of Wisconsin-Madison) \n\n\n\nAbstract: Emerging single cell technologies that simultaneously capture long-range interactions of genomic loci together with their DNA methylation levels are advancing our understanding of 3D genome structure and its interplay with the epigenome at the single cell level. While methods to analyze data from single cell high throughput chromatin conformation capture (scHi-C) experiments are maturing\, methods that can jointly analyze multiple modalities with scHi-C data are lacking. In this talk\, I present two tensor modeling frameworks: Muscle and SHOPS\, to jointly analyze 3D conformation and DNA methylation data measured at the single cell level. First\, I present Muscle\, a joint decomposition of Multiple single cell tensors. Muscle is a novel tensor decomposition method that can integrate the scHi-C and DNA methylation modalities with a direct interpretability. Next\, I introduce SHOPS\, Sparse Higher Order Partial Least Squares\, which provides an inference on the direct association between Hi-C and DNA methylation. SHOPS is a new tensor response regression method to simultaneously achieve denoising of the scHi-C tensor and selecting the most relevant methylation sites with dimension reduction. \n\n\n\nAbout the speaker: Kwangmoon Park is a Statistics Ph.D. Candidate at the University of Wisconsin-Madison. He is currently working on statistical genomics and high dimensional statistics with Professor Sündüz Keleş. Before joining UW-Madison\, he earned a master’s degree in Statistics at the Yonsei University in 2020. He earned a B.A. in Economics and Statistics at Yonsei University in Korea and studied Economics as an exchange student at Erasmus Universiteit Rotterdam in the Netherlands. Kwangmoon Park is mainly interested in questions related to understanding how genes are regulated by distal regions in the genome\, particularly by functional non-coding regions. For that purpose\, he develops statistical tools for analyzing High-dimensional genomic data\, including Hi-C and HiChIP\, and for linking diverse types of genomic or epigenomic data with better statistical interpretation. The statistical methodologies he works on are related to tensor factorization/regression and dimension reduction techniques\, including Partial Least Squares. \n\n\n\nJoin on Zoom: https://uni-sydney.zoom.us/j/84087321707
URL:https://spds.sydney.edu.au/event/high-dimensional-tensor-methods-for-multi-modal-single-cell-genomics-data/
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