Modelling Microbial Data with Time, Treatment and/or Space Variation

This talk is motivated by issues arising from microbial oceanic data that biological researchers have been collecting to understand variation in response to environmental changes. The data typically consists of counts of OTUs (operational taxonomic units) from ocean samples varying either in time, treatment and/or space. At any given observation there will typically be at least 1000 OTUs of potential interest. In particular I will describe some data arising from an experimental study of microbial organisms in the oceans to assess the effect of enhanced carbon loading. I will indicate briefly our approach to modelling the data to take into account both the experimental and temporal variation. In our view, an essential first step is to carry out dimension reduction via clustering based on the results of Poisson generalized linear models. Then we can carry out the tests for any significant experimental effect. In this data, it is likely that only a small subset of the OTUs may show a response to the carbon loading. During the talk, it will be clear that there are still some unresolved statistical issues on which we welcome feedback. I should note that although the data I have is from the ocean, similar issues will arise with microbial data coming from many other environments such as the human gut.