Modern study of biological systems delivers high-dimensional data as an outcome, such as expression data from RNA sequencing, with each response recorded resulting in large and complex datasets. Statistical analysis of these high-dimensional datasets needs to consider the within-sample dependences, such as spatial correlation within the brain. Current experimental design strategy typically uses parallel groups of animals to compare a single variable but longitudinal studies are also employed, where an individual animal or litter are measured repeatedly over time. The variable measured in these studies is often related to the amount of time between measurements, demonstrating time dependence. Statistical models for the design and analysis of longitudinal studies with high-dimensional outcomes needs to consider both types of dependences. The framework of these models is yet to be established.
Why we funded it
This PhD Studentship aims to develop a novel statistical model for the design and analysis of high-dimensional longitudinal animal studies. Well-designed longitudinal studies can take advantage of time dependence to gain statistical power and reduce the number of animals required for the study.
The exact number of animals that could be reduced depends on the correlation between the repeated measures. With a correlation value of 0.2, the numbers of animals needed for the study can be reduced by 20% compared to a parallel group experimental design. To demonstrate the potential of this work, historic mouse functional brain imaging data will be analysed and an accurate reduction potential calculated retrospectively.
The statistical model to be developed in this proposal will be based on generalised linear mixed models and Gaussian processes. Once developed, the statistical power will be evaluated by analysing brain imaging data from a genetic mouse model with behaviours relevant to both schizophrenia and autism. The brain shows spatial correlation patterns which statistical models must take into account and this will be used to inform the statistical model developed in this proposal. The model will allow the identification of statistically significant brain metabolism variation and gene expression over time and show differences between the mutant and wild type mice. This will allow for new insights into the developmental regulation of brain function in mice.
Principal investigatorProfessor Peter Diggle
Co-InvestigatorDr Frank Dondelinger
Dr Neil Dawson