Sydney Precision Data Science Centre

Zero-Shot Foundation Model for a Universal Gene Expression Atlas of Human Tissue: Unveiling Clinically Relevant Cell States and Disease-Specific Spatial Niches
2025-03-03 13:00 - 14:00 (Australia/Sydney)
Statistical Bioinformatics SeminarDr Xiaomeng Wan, HKUST

The rapid accumulation of single-cell datasets from diverse organs and tissues presents significant opportunities for understanding complex diseases, yet challenges remain in effectively analyzing this wealth of information and further leveraging it to various data types, including spatial transcriptomics (ST) and bulk RNA-seq datasets. Here, we introduce UniGeneX, a generative single-cell foundation model designed to reconstruct a universal gene expression profile from extensive transcriptomic data. UniGeneX minimizes batch effects while preserving biological variability, enabling the identification of shared gene programs across tumor samples. By providing consistent cell type labels and leveraging biological patterns from training data, UniGeneX facilitates the discovery of disease-specific cell niches in spatial and key cell states associated with clinical outcomes. Our model addresses existing limitations in current single-cell foundation models by focusing on a universal gene expression framework rather than merely learning embeddings for downstream tasks. We demonstrate the effectiveness of UniGeneX in characterizing disease-relevant cell states in glioma and idiopathic pulmonary fibrosis (IPF), ultimately advancing our understanding of the mechanisms underlying complex diseases.

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Dr Xiaomeng Wan

Dr Xiaomeng Wan is currently a Postdoctoral Associate in the Department of Mathematics at the Hong Kong University of Science and Technology (HKUST), under the guidance of Prof Can Yang. She earned her PhD from HKUST under the mentorship of Prof Can Yang. Her research centres on statistical machine learning and deep learning, particularly exploring their applications in the analysis of transcriptomics datasets.

Computational Approaches in Functional Genomics: Understanding Gene Regulation and Development
2025-02-24 13:00 - 14:00
Statistical Bioinformatics SeminarDr Luca Pinello, Harvard Medical School

In this talk, Dr Luca Pinello will present an overview of his lab’s recent work at the intersection of CRISPR genome editing, single-cell omics assays, and generative AI to elucidate gene regulation. His lab’s research focuses on developing computational methods to investigate genotype-phenotype relationships at high resolution, infer dynamic gene regulatory networks, and integrate multi-omics data. By leveraging state-of-the-art technologies and advanced computational approaches, his lab aims to uncover the complexities of cellular heterogeneity, identify key regulatory elements and transcription factors, and understand the interplay between chromatin structure and function in normal development and disease.

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Dr Luca Pinello

Luca Pinello is a computational biologist and leader in developing computational methods for functional genomics, genome editing and single cell technologies. He holds a Ph.D. in Mathematics and Computer Science from University of Palermo, Italy. He is currently an Associate Pathologist at Massachusetts General Hospital (MGH) and an Associate Professor of Pathology at Harvard Medical School. He is also part of the MGH Center for Cancer Research and an Associate Member of the BROAD Institute of MIT and Harvard. He has developed several foundational computational tools in the field of genome editing for the design (CRISPRme, CRISPRitz, PrimeDesign), quantification (CRISPResso 1 and 2), and analyses of coding and non-coding tiling screens (CRISPRO, CRISPR-SURF). He was awarded one of the first NIH R35 Genomic Innovator Awards, a prestigious grant supporting highly innovative researchers working on important problems in genomics.

Incorporating experimental medicine into single-cell multi-omics
2024-12-16 13:00 - 14:00 | Mackenzie Room, Level 6, Charles Perkins Centre, University of Sydney, Johns Hopkins Drive, University of Sydney, NSW, 2006, Australia
Special Statistical Bioinformatics Seminar

Dr Jacqueline Siu, Kennedy Institute of Rheumatology, University of Oxford

About the seminar: Coordinated global efforts to create a comprehensive map of the human body using single-cell technologies has opened up avenues in computational biology and AI. However, there remains a pressing need to increase the data diversity from the ancestral diversity of participants to increasing the types of immune perturbations captured. This talk will focus on incorporating single-cell multi-omics to different experimental medicine approaches–such as deceased organ donors, vaccine and xenogeneic-antigen challenge trials–in order to understand healthy immune responses in lymphoid organs.

About the speaker: Jacqueline Siu received her PhD at the University of Cambridge where she combined bioinformatics and experimental medicine to understand the function and differentiation of homeostatic human B cells in matched secondary lymphoid organs. To extend her interest in human tissue immunity especially after perturbation, Jacqueline joined the labs of Professor Mark Coles/ Professor Calliope Dendrou as a postdoctoral fellow, where she explored the influenza vaccine response of human axillary lymph nodes. In 2023, Jacqueline received the Wellcome Trust Early Career award to continue her interest in integrating human challenge models and single-cell genomics to understand early responding human B cells and their impact on long-lived antibodies.

Veridical data science and alignment in medical AI
2024-12-13 14:00 - 15:00 | Lecture Theatre 173, Carslaw Building (F07.01.173)
Professor Bin Yu, Statistics, EECS, Center for Computational Biology, Simons Institute for the Theory of Computing, UC Berkeley

About the seminar

Alignment and trust are crucial for the successful integration of AI in healthcare including digital twin projects, a field involving diverse stakeholders such as medical personnel, patients, administrators, public health officials, and taxpayers, all of whom influence how these concepts are defined. This talk presents a series of collaborative medical case studies where AI algorithms progressively become, from transparency to more opaque thus with increasing difficulty of alignment assessment. These range from tree-based methods for trauma diagnosis, to LLM-based emergency department co-pilot, and mechanistic circuits for structured data extraction from pathology reports. They are guided by Veridical Data Science (VDS) principles—Predictability, Computability, and Stability (PCS)—for the goal of building trust and interpretability, enabling doctors to assess alignment. The talk concludes with a discussion on applying VDS to digital twins and medical foundation models and next steps for evaluating AI algorithm alignment in healthcare.

About Bin Yu

Bin Yu is CDSS Chancellor’s Distinguished Professor in Statistics, EECS, and Computational Biology, and Scientific Advisor at the Simons Institute for the Theory of Computing, all at UC Berkeley. Her research focuses on the practice and theory of statistical machine learning, veridical data science, and solving interdisciplinary data problems in neuroscience, genomics, and precision medicine. She and her team have developed algorithms such as iterative random forests (iRF), stability-driven NMF, and adaptive wavelet distillation (AWD) from deep learning models. She is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She was a Guggenheim Fellow, IMS President, and delivered the IMS Rietz and Wald Lectures and Distinguished Achievement Award and Lecture (formerly Fisher Lecture) of COPSS. She holds an Honorary Doctorate from The University of Lausanne

Indigenous Australian genomes show deep population structure and abundant novel variation
2024-11-25 13:00 - 14:00
Special Judith and David Coffey Seminar

Prof Stephen Leslie, The University of MelbourneAbstract: Without the inclusion of diverse genetic ancestries in reference resources, inequity in research and clinical practice risks being entrenched. A handful ethnicities have been the focus of genomic research to date, and Indigenous Australians are virtually absent from global reference panels and genomic analyses. The National Centre for Indigenous Genomics (NCIG) has collected genomic data from four Indigenous Australian communities from distinct regions of Australia. This is the largest sample of Indigenous Australian whole genomes to date, made possible by careful engagement and consultation with the communities, setting new standards for best practice in Indigenous research. In this talk I will show analyses of the genomes of 159 individuals from these four communities, investigating patterns of genetic variation and diversity within this dataset, as well as the causes and consequences of these patterns.

We discover substantial uncharacterised genetic variation in Indigenous Australians when compared to worldwide populations, with implications for the utility of global reference panels and databases for conducting research within these communities. We show the variation is shaped by exceptionally strong population structure across Australia and explore its features and the factors that have caused such structure. Our analysis emphasizes the distinctiveness of Indigenous Australians in a worldwide context with consequences for the design and implementation of genomic research and clinical practice in Indigenous communities.

About the speaker: Prof. Stephen Leslie is a statistician working in the field of mathematical genetics. He is currently Professor of Statistical Genomics in the School of Mathematics and Statistics, and Melbourne Integrative Genomics, at the University of Melbourne. Stephen has made key contributions to detecting, understanding, and controlling for population differences in genetic data, including his landmark papers on fine-scale population genetic differentiation in the UK, and more recently for Indigenous Australians. He also developed the first methods for typing HLA and KIR alleles (major immune-associated gene families) from SNP data, making these important variants available for large datasets world-wide, including as part of the main data release for the UK Biobank. Stephen has won several major awards for his work including the Woodward Medal of the University of Melbourne, and the Moran Medal of the Australian Academy of Science.

Stephen’s research focuses on understanding the association of population genetic variation and history/demography; uncovering the relationship of immune-associated loci and autoimmune disease; and performing statistically rigorous analyses of the relationship of genetic variants to disease.

BioCAsia 2024
2024-11-07 08:00 - 2024-11-08 17:00
The 2024 Bioconductor Asia conference aims to bring together researchers and scientists to exchange scientific knowledge and foster collaboration within the bioinformatics community both across the Asia-Pacific region and globally. The conference will also offer a series of hands-on workshops on R and Bioconductor, aimed at advancing education and training in computational biomedical sciences. Visit the website to find out more https://biocasia2024.bioconductor.org/

ABACBS Conference
2024-11-04 08:00 - 2024-11-06 17:00
The ninth annual Australian Bioinformatics and Computational Biology Society Conference will be hosted at the University of Sydney 4-6 November 2024. Our centre director, research leaders and ECRs are members of the convening committee. Visit the conference website for full details https://www.abacbs.org/conference2024/home

COMBINE Student Symposium
2024-11-04 08:00 - 17:00
The COMBINE Student Symposium is back in 2024! Visit the symposium website for all the details https://www.combine.org.au/symp/symposium-2024/

Weekly Seminar - Week 1
2024-10-18 08:00 - 17:00 | Eastern Avenue Lecture Theatre, Eastern Avenue - The University of Sydney, Camperdown, NSW, 2006
Statistical Bioinformatics Seminar

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Harnessing the power of AI in biotechnology, personalised medicine, drug development and beyond
2024-10-14 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 David Ascher (University of Queensland)

Abstract: We have developed a comprehensive computational platform that uses graph-based signatures to represent the wild-type environment of a residue in order to predict the structural and functional effects of mutations. This platform has been used to explore the effects of genetic disease and drug resistance mutations on protein folding, stability, dynamics and interactions, and their links to mutational tolerance and phenotypes. Mutations leading to larger molecular consequences, tended to be rarer, and needed the presence of compensatory mutations balancing these fitness costs to become fixed in a population.

We have now successfully clinically translated methods that use insights on the 3D effects of mutations to guide patient risk management in genetic diseases, and in the pre-emptive detection of drug resistance mutations in tuberculosis (rifampicin and pyrazinamide resistance). It has also been applied as part of drug development pipelines to guide design of drugs less prone to resistance.

This work has highlighted that structural bioinformatics tools, when applied in a systematic, integrated way, can provide a powerful and scalable approach for predicting structural and functional consequences of mutations in order to reveal molecular mechanisms leading to clinical and experimental phenotypes. These computational tools are freely available (http://biosig.unimelb.edu.au/biosig/tools).

About the speaker: David Ascher is Deputy Associate Dean of Research and Director of Biotechnology Program at The University of Queensland, Deputy Director of the Australian Centre for Ecogenomics and Head of the Computational Biology and Clinical Informatics laboratory at the Baker Institute. David’s research focus is in modelling biological data to gain insight into fundamental biological processes. One of his primary research interests has been developing tools to unravel the link between genotype and phenotype, using computational and experimental approaches to understand the effects of mutations on protein structure and function. His group has developed a platform of 65 widely used programs for assessing the molecular consequences of coding variants. This platform has now been successfully translated into clinical use to guide the diagnosis, management and treatment of a number of hereditary diseases, rare cancers and drug resistant infections, and into industry as part of drug development pipelines to guide design of safer and more effective therapeutics.

Chatting About Pathology
2024-09-30 13:00 - 14:00
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
