WEHI Special Bioinformatics Seminar hosted by Dr Chin Wee Tan

Dharmesh D Bhuva

Mangiola Lab – Computational Systems Oncology Program – South Australian Immunogenomics Cancer Institute (SAiGENCI) – University of Adelaide

Modelling spatial molecular data for normalisation and power analysis.

Davis Auditorium

Join via ZOOM

Dr Dharmesh D Bhuva is an early-career computational systems biologist who is passionate about understanding how complex systems of gene regulation and signalling lead to diverse molecular phenotypes in healthy and diseased tissues. He completed his PhD in 2020 under the supervision of Prof. Edmund Crampin and Prof. Melissa Davis at the University of Melbourne and WEHI. His PhD focused on developing new systems biology approaches to study molecular function and gene regulation in cancer systems. He then undertook his post-doctoral studies with Prof. Melissa Davis at the world-renowned WEHI Bioinformatics division, where he embarked on developing novel approaches to study cancer tissues using spatial molecular data. In 2023, he joined the computational systems oncology division at the South Australian Immunogenomics Cancer Institute (SAiGENCI) to continue his cutting-edge research in developing computational approaches to analyse high resolution spatial molecular data. He has published his bioinformatics approaches in NAR and Genome Biology and has implemented them into open-source software that have had more than 103,000 downloads. He was part of a team that was recently awarded a MRFF to identify spatial biomarkers of response to therapy in lung cancer, and has recently been awarded an NHMRC EL1 investigator grant to identify topological biomarkers in cancer.

Spatial resolution of molecular measurements has revolutionised biological studies while posing a significant informatics challenge. Advances in commercial products have increased the spatial resolution and throughput of measurements obtained, however, these are often coupled with significant costs. To better understand the properties of these expensive spatial molecular datasets, we need to understand the spatial nature of measurements and refrain from imposing cellular abstractions where possible. I will begin by describing our investigation into the total density of measurements (library size) in spatial transcriptomics datasets and show how it confounds biology. As a result of this confounding effect, library size normalisation using current methods results in poorer domain identification. Next, I will present our newly developed model, SpaNorm, that uses thin plate splines and a regularised generalised linear model (GLM) to model transcript counts and subsequently adjust library size effects by computing percentile adjusted counts. Our spatially aware library size normalisation method can adjust library size effects while retaining biological variation in the tasks of spatial domain identification and spatially variable gene calling. Finally, I will show how SpaNorm can be repurposed to model cell counts and estimate cellular rates for power analysis where the spatial distribution patterns of cells play a crucial role. Collectively, these results outline the strength of modelling spatial variation using splines and GLMs and demonstrate their broad utility.

All Welcome!