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BMC Proceedings (1)
Journal of the Royal Statistical Society. Series B, Statistical Methodology (1)
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (1)
Chun, Hyonho (3)
Keleş, Sündüz (2)
Engelman, Corinne D (1)
Kuan, Pei Fen (1)
Payseur, Bret A (1)
Shim, Heejung (1)
Year of Publication
CMARRT: A Tool for the Analysis of ChIP-chip Data from Tiling Arrays by Incorporating the Correlation Structure
Kuan, Pei Fen
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Whole genome tiling arrays at a user specified resolution are becoming a versatile tool in genomics. Chromatin immunoprecipitation on microarrays (ChIP-chip) is a powerful application of these arrays. Although there is an increasing number of methods for analyzing ChIP-chip data, perhaps the most simple and commonly used one, due to its computational efficiency, is testing with a moving average statistic. Current moving average methods assume exchangeability of the measurements within an array. They are not tailored to deal with the issues due to array designs such as overlapping probes that result in correlated measurements. We investigate the correlation structure of data from such arrays and propose an extension of the moving average testing via a robust and rapid method called CMARRT. We illustrate the pitfalls of ignoring the correlation structure in simulations and a case study. Our approach is implemented as an R package called CMARRT and can be used with any tiling array platform.
ChIP-chip; moving average; autocorrelation; false discovery rate
Genome-wide association studies using single-nucleotide polymorphisms versus haplotypes: an empirical comparison with data from the North American Rheumatoid Arthritis Consortium
Engelman, Corinne D
Payseur, Bret A
The high genomic density of the single-nucleotide polymorphism (SNP) sets that are typically surveyed in genome-wide association studies (GWAS) now allows the application of haplotype-based methods. Although the choice of haplotype-based vs. individual-SNP approaches is expected to affect the results of association studies, few empirical comparisons of method performance have been reported on the genome-wide scale in the same set of individuals. To measure the relative ability of the two strategies to detect associations, we used a large dataset from the North American Rheumatoid Arthritis Consortium to: 1) partition the genome into haplotype blocks, 2) associate haplotypes with disease, and 3) compare the results with individual-SNP association mapping. Although some associations were shared across methods, each approach uniquely identified several strong candidate regions. Our results suggest that the application of both haplotype-based and individual-SNP testing to GWAS should be adopted as a routine procedure.
Sparse partial least squares regression for simultaneous dimension reduction and variable selection
Journal of the Royal Statistical Society. Series B, Statistical Methodology
Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data.
Chromatin immuno-precipitation; Dimension reduction; Gene expression; Lasso; Microarrays; Partial least squares; Sparsity; Variable and feature selection
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