Modeling epigenetic modifications under multiple treatment conditions
Computational Statistics & Data Analysis
Multivariate segmentation in the analysis of transcription tiling array data
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
International Journal of Data Mining and Bioinformatics
Joint adaptive mean-variance regularization and variance stabilization of high dimensional data
Computational Statistics & Data Analysis
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Motivation: Tiling array is a new type of microarray that can be used to survey genomic transcriptional activities and transcription factor binding sites at high resolution. The goal of this paper is to develop effective statistical tools to identify genomic loci that show transcriptional or protein binding patterns of interest. Results: A two-step approach is proposed and is implemented in TileMap. In the first step, a test-statistic is computed for each probe based on a hierarchical empirical Bayes model. In the second step, the test-statistics of probes within a genomic region are used to infer whether the region is of interest or not. Hierarchical empirical Bayes model shrinks variance estimates and increases sensitivity of the analysis. It allows complex multiple sample comparisons that are essential for the study of temporal and spatial patterns of hybridization across different experimental conditions. Neighboring probes are combined through a moving average method (MA) or a hidden Markov model (HMM). Unbalanced mixture subtraction is proposed to provide approximate estimates of false discovery rate for MA and model parameters for HMM. Availability: TileMap is freely available at http://biogibbs.stanford.edu/~jihk/TileMap/index.htm Contact: whwong@stanford.edu Supplementary information:http://biogibbs.stanford.edu/~jihk/TileMap/index.htm (includes coloured versions of all figures)