Multivariate segmentation in the analysis of transcription tiling array data.

Tiling DNA microarrays extend current microarray technology by probing the non-repeat portion of a genome at regular intervals in an unbiased fashion. A fundamental problem in the analysis of these data is the detection of genomic regions that are differentially transcribed across multiple conditions. We propose a linear time algorithm based on segmentation techniques and linear modeling that can work at a user-selected false discovery rate (FDR). It also attains a fourfold sensitivity gain over the only competing algorithm when applied to a whole genome transcription data set spanning the embryonic development of Drosophila melanogaster.