Led by Dr Stefano Mangiola at the South Australian Immunogenomics Cancer Institute (SAiGENCI), our research group applies state-of-the-art computational methods and artificial intelligence to unravel the immune system’s role in cancer progression and treatment resistance. Our work has been published in prestigious journals such as PNAS, Genome Biology, and Nature Methods.
Our group is at the forefront of integrating computational biology with multiomic data production. We focus on:
- Spatial and Single-Cell Omics Integration: Combining spatial transcriptomics and single-cell analyses to capture the tumour microenvironment.
- Machine Learning & AI in Biology: Developing large-language AI models and employing statistical and machine learning techniques to predict therapeutic outcomes.
- Cancer Immunodiagnosis: Analysing immune-cell interactions to understand and predict therapy resistance in breast and other cancers.
- Tidy Data Analysis: Using R tidy programming to streamline multiomics analyses in
Bioconductor
, as demonstrated in ourtidyomics
ecosystem. - Large-Scale Inference: Constructing scalable infrastructure for analysing expansive single-cell datasets across diverse human populations.
Our group has contributed to several open-source projects that support multiomic and computational analyses:
- Tidyomics: An R ecosystem for tidy data analysis across multiple omic types.
- sccomp A Bayesian mixed effect model for single-cell composition and variability analysis. Test differences in cell-type proportion for complex sigle-cell and spatial datasets.
- CuratedAtlasQuery & HPCell: Platforms for large-scale single-cell data analysis and high-performance computing integration.
We welcome collaboration from fellow researchers, students, and developers. Dr Mangiola is available to supervise Masters and PhD projects; please email the supervisor contact for further discussion regarding opportunities.
For further information, enquiries, or collaboration opportunities, please contact us via the official email addresses provided on our website.