Sydney Precision Data Science Centre

Chatting About Pathology
2024-09-30 13:00 - 14:00 (Australia/Sydney)
Statistical Bioinformatics Seminar

Speaker: Dr Drew Williamson (Emory University)

Abstract: Generative models such as ChatGPT have catapulted AI further into the mainstream than ever before, while interested parties in healthcare have begun to test these models in medical settings. However, these models are rarely purpose-built for medical applications and the majority of those that do exist are targeted at a broader segment of healthcare than any individual provider practices. I’ll discuss the design, training, and evaluation of PathChat, our multimodal generative model created specifically for pathology. I’ll also discuss possible future directions in this line of inquiry.

About the speaker: Drew Williamson completed a BA in Mathematics at Oberlin College, an MD at Case Western Reserve University, residency in Anatomic Pathology at Brigham & Women’s Hospital, and fellowships in Molecular Genetic Pathology and Clinical Informatics at Mass General Brigham. This training was followed by postdoctoral research in the lab of Faisal Mahmood, PhD at Brigham & Women’s Hospital and Harvard Medical School. A board certified pathologist, Drew’s research focuses on the applications of deep learning-based methods to pathology data, from histology images to genomics to, most recently, natural language. He is now an Assistant Professor in the department of Pathology & Laboratory Medicine at Emory University.

Systematic comparison of sequencing-based spatial transcriptomic methods with cadasSTre and SpatialBen
2024-04-22 13:00 - 14:00 | Charles Perkins Centre, University of Sydney, Johns Hopkins Dr, Sydney, NSW, 2050, Australia
Statistical Bioinformatics Seminar: Judith and David Coffey Speaker

Speaker: Prof Matthew Ritchie (WEHI)

Abstract: 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.

About 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.

High dimensional tensor methods for multi-modal single cell genomics data
2024-04-15 13:00 - 14:00
Statistical Bioinformatics Seminar

Speaker: Kwangmoon Park (University of Wisconsin-Madison)

Abstract: 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.

About 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.

Join on Zoom: https://uni-sydney.zoom.us/j/84087321707
