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DTSTART;TZID=Australia/Sydney:20240422T130000
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DTSTAMP:20260417T084505
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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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