A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.

Field applications of the second-generation Environmental Sample Processor (ESP) for remote detection of harmful algae: 2006-2007

We assess the application of the second-generation Environmental Sample Processor (ESP) for the detection of harmful algal bloom (HAB) species in field and laboratory settings using two molecular probe techniques: a sandwich hybridization assay (SHA) and fluorescent in situ hybridization (FISH). During spring 2006, the first time this new instrument was deployed, the ESP successfully automated application of DNA probe arrays for various HAB species and other planktonic taxa, but non-specific background binding on the SHA probe array support made results interpretation problematic. Following 2006, the DNA array support membrane that we were using was replaced with a different membrane, and the SHA chemistry was adjusted. The sensitivity and dynamic range of these modifications were assessed using 96-well plate and ESP array SHA formats for several HAB species found commonly in Monterey Bay over a range of concentrations; responses were significantly correlated (p < 0.01). Modified arrays were deployed in 2007. Compared to 2006, probe arrays showed improved signal:noise, and remote detection of various HAB species was demonstrated. We confirmed that the ESP and affiliated assays can detect HAB populations at levels below those posing human health concerns, and results can be related to prevailing environmental conditions in near real-time.

Bayesian two-sample tests

In this paper, we present two classes of Bayesian approaches to the two-sample problem. Our first class of methods extends the Bayesian t-test to include all parametric models in the exponential family and their conjugate priors. Our second class of methods uses Dirichlet process mixtures (DPM) of such conjugate-exponential distributions as flexible nonparametric priors over the unknown distributions.