A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes: An Application of Bayesian Hierarchical Clustering of Curves

Malaria represents one of the major worldwide challenges to public health. A recent breakthrough in the study of the disease follows the annotation of the genome of the malaria parasite Plasmodium falciparum and the mosquito vector 1 Anopheles. Of particular interest is the molecular biology underlying the immune response system of Anopheles which actively fights against Plasmodium infection. This paper reports a statistical analysis of gene expression time profiles from mosquitoes which have been infected with a bacterial agent. Specifically, we introduce a Bayesian model-based hierarchical clustering algorithm for curve data to investigate mechanisms of regulation in the genes concerned; that is, we aim to cluster genes having similar expression profiles. Genes displaying similar, interesting profiles can then be highlighted for further investigation by the experimenter. We show how our approach reveals structure within the data not captured by other approaches. One of the most pertinent features of the data is the sample size, which records the expression levels of 2771 genes at six time points. Additionally, the time points are unequally spaced and there is expected non-stationary behaviour in the gene profiles. We demonstrate our approach to be readily implementable under these conditions, and highlight some crucial computational savings that can be made in the context of a fully Bayesian analysis. ∗Nicholas Heard (Email: [email protected]) and David Stephens (Email: [email protected]) are Lecturers in Statistics, Department of Mathematics, South Kensington Campus, Imperial College London, SW7 2AZ, U.K.; Christopher Holmes (Email: [email protected]) is Lecturer in Statistics, Oxford Centre for Gene Function, Department of Statistics, University of Oxford, 1 South Parks Rd, Oxford, OX1 3TG, U.K. N. A. Heard was supported by grant 065822 from the Wellcome Trust. An organism that spreads an infectious disease 1