  Microarrays have been developed that tile the entire nonrepetitive genomes of
many different organisms, allowing for the unbiased mapping of active
transcription regions or protein binding sites across the entire genome. These
tiling array experiments produce massive correlated data sets that have many
experimental artifacts, presenting many challenges to researchers that require
innovative analysis methods and efficient computational algorithms. This paper
presents a doubly stochastic latent variable analysis method for transcript
discovery and protein binding region localization using tiling array data. This
model is unique in that it considers actual genomic distance between probes.
Additionally, the model is designed to be robust to cross-hybridized and
nonresponsive probes, which can often lead to false-positive results in
microarray experiments. We apply our model to a transcript finding data set to
illustrate the consistency of our method. Additionally, we apply our method to
a spike-in experiment that can be used as a benchmark data set for researchers
interested in developing and comparing future tiling array methods. The results
indicate that our method is very powerful, accurate and can be used on a single
sample and without control experiments, thus defraying some of the overhead
cost of conducting experiments on tiling arrays.
