ANU Biometrics Seminar Series
The series aims to foster knowledge exchange at the intersection of biology, statistics, computing, and data science. We hope to use this as a regular platform for the community to share their work, spark meaningful discussions and cultivate interdisciplinary collaborations. To facilitate participation from across campus, the seminars will primarily be held online via Zoom. If you are interested in organising or joining a watch party, please get in touch.
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This series is organised by Dr Patrick Weihao Li ✨
Oct 20, 2025
A predictive model for genotype x environment x management practice (GxExM) interactions incorporating environmental covariates
Michael Mumford (Queensland Department of Primary Industries)
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In field crops research, genotype by environment (G×E) interactions are ubiquitous. The development of statistical methods to model the genotype by environment interaction using environmental covariates (ECs) has mostly occurred within the context of crop breeding. In agronomic research, measuring the impact of management practice (M) on genotype performance is also a key objective, giving rise to the genotype by environment by management practice (G×E×M) interaction. A one-stage analysis approach, implemented in a linear mixed model framework, is presented, which incorporates ECs to disentangle the G×E×M interaction effects. The linear mixed model framework allows adjustments for design effects and spatial field trend, along with the estimation of heterogeneous residual variance across environments. An application of the methodology will be demonstrated using a series of sorghum agronomy field experiments. Recently, a complementary R-package called ecreml was developed to enable researchers to easily apply the developed statistical methodology to their own multi-environment trial data consisting of G×E×M interaction effects, and is publicly available to install via GitHub.
Bio:
Michael is a biometrician at the Queensland Department of Primary Industries, with extensive experience collaborating with agricultural researchers on a range of Grain Research Development Corporation (GRDC) projects; initially through the Statistics for the Australian Grains Industry (SAGI) project and more recently the Analytics for the Australian Grain Industry (AAGI) initiative. Michael’s research interests are in statistical methodological development, with a focus on developing methodologies that are directly applicable to agricultural experiments. Michael is also completing PhD studies part-time currently through the University of Queensland, focused on developing improved statistical methodologies for the analysis of count data arising from comparative agricultural experiments using generalised linear mixed models.
Oct 7, 2025
Design and Spatial Analysis of On-Farm Strip Trials: From Principles to Simulation Evaluations
Zhanglong Cao (Curtin University)
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This study explores statistical strategies for the design and analysis of on-farm experiments (OFE), grounded in established principles of experimental design and spatial modelling. Through simulation studies, we assess various approaches, including design layouts combined with Geographically Weighted Regression (GWR) for continuous response variables and Linear Mixed Models (LMM) for categorical treatments. Our results align with key findings in the literature, emphasising the importance of appropriate design and modelling choices for effectively capturing spatial heterogeneity. Additionally, we compare two trial types—large-strip trials and stacked replicated trials—and highlight the significance of data granularity, which informs data collection strategies for industry partners. The findings advocate for appropriate trial designs for different purpose, and increased within-trial sampling, rather than reliance solely on average values to better reflect local variability and enhance inference accuracy. This work underscores the critical role of tailored statistical methods in improving the reliability and practical applicability of OFE outcomes.
Aug 15, 2025
How will future climate change affect wheat yield and nutritional quality?
Zixiong Zhuang (Australian National University)
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In the coming decades, climate change will continue to bring higher atmospheric CO2 concentrations as well as warmer air temperatures. However, the combined effects of elevated CO₂ (eCO2) and elevated temperature (eT) on wheat grain yield and nutrition quality remain largely unknown. When tested separately, eCO2 increases wheat yield and decreases grain nutritional quality, whereas eT decreases yield but with unknown effects on grain quality. To better understand the concurrent treatment of eCO2 and eT on wheat performances as per realistic future climate conditions, this talk presents early insights into one of the largest experiments to expand our knowledge in this area — testing 90 genetically diverse wheat lines under projected climate conditions by the year 2100 (800 ppm CO2 and + 5°C). This talk presents preliminary results from the first year’s experiment of this project. I outline both the traditional biometrics and high-throughput phenotyping (HTP) traits captured to explain the variations in plant responses to future climate conditions. I demonstrate analyses that support early understanding about the underlying biological mechanisms contributing to variations in wheat yield and nutrition qualities that can be studied further in future experiments.
Bio:
Zixiong is a second-year PhD student at the Research School of Biology (RSB). He is supervised by Professor Danielle Way where he focuses on plant physiological responses to future climate change in Australian spring wheat lines. Zixiong completed his Master’s degree with Professor Justin Borevitz at the Australian National University. During Zixiong’s Master’s degrees, he discovered genomic structural variants using population-level long-read sequencing on wild Eucalyptus pangenomes. He joined the Way Lab in 2023, transitioning his focus from population genomics to ecophysiology and plant phenomics.
Aug 1, 2025
Novel methods for analysing single cell gene expression data
Yidi Deng (Australian National University)
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This talk will introduce three novel methods we developed to address key challenges in single-cell RNA-seq (scRNA-seq) data analysis. Sincast is an imputation method that denoises scRNA-seq data to enhance the robustness of downstream statistical analyses. Its utility is highlighted through comprehensive benchmarking of cell identity against bulk transcriptomic references. StableMate is a stability-focused regression framework that identifies functional dependencies in gene expression that are either conserved or meaningfully variable across biological contexts. When applied to scRNA-seq data, StableMate enhances reproducibility and provides regulatory insights into co-expression analyses. NeighbourNet constructs cell-specific gene regulatory networks, facilitating the fine-grained characterization of gene regulatory dynamics at the single-cell level and offering a new perspective on analysing scRNA-seq through cellular variation in co-expression.
Bio:
Yidi Deng is a statistical bioinformatician and new post-doctoral fellow, working with Dr Emi Tanaka to uncover genomic insights that support Australia’s grain-industry research. Yidi completed a PhD in applied statistics at the University of Melbourne, where he developed novel methods for multi-omics data integration and variable selection, aimed at deciphering gene-regulatory mechanisms in the context of developmental biology and immunology. Yidi’s overall research interests is to translate advanced statistical approaches into intuitive, practical software that biologists can easily adopt without specialized statistical or computational expertise.