Project VII: Simulation-based analysis of the spatial configuration of nuclear bodies.

A key biological objective is to understand and represent structural characteristics of cell nuclei in order to gain insight into the various mechanisms of cell organization. This can to be achieved using statistical hypothesis testing, inference and classification to assess the evidence relating to the configuration of cellular and nuclear objects.

Confocal microscopy images of mammalian interphase cell nuclei stained with a variety of nuclear components are available and can be used to investigate their spatial configuration. Spatial probabilistic models are now routinely used in biostatistics and epidemiology, and can be used for the analysis of biological data. Advances in the field of simulation have made simulation-based more attractive, and this is the basis of the inference mechanisms in this project.

The set of squared distances between pairs of loci in the nucleus can be used to define simulation-based statistical tests. Simulation-based hypothesis testing is characterized by need for a good choice of test statistics to ensure high statistical power, and efficient computation of null distribution through simulation; to test a specific null hypothesis, simulation methods can be implemented. For tests on single cells, Monte Carlo tests will often be sufficient, but when a random sample of cells are available, it will be necessary and important to be able to combine information across the samples in order to carry out (for example) likelihood ratio tests or likelihood/Bayesian inference.