Scan statistics analysis for detection of introns in time-course tiling array data
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Anat Reiner-Benaim
,
Ronald W. Davis
Abstract
A tiling array yields a series of abundance measurements across the genome using evenly spaced probes. These data can be used for detecting sequences that exhibit a particular behavior. Scanning window statistics are often employed for testing each probe while accounting for local correlation and smoothing noisy measurements. However, window testing may yield false probe discoveries around the sequences and false non-discoveries within the sequences, resulting in biased predicted intervals. We propose to avoid this problem by stipulating that a sequence of interest can appear at most once within a defined region, such as a gene; thus, only one window statistic is considered per region. This substantially reduces the number of tests and hence, is potentially more powerful. We compare this approach to a genome-wise scan that does not require pre-defined search regions, but considers clumps of adjacent probe discoveries. Simulations show that the gene-wise search maintains the nominal FDR level, while the genome-wise scan yields FDR that exceeds the nominal level for low interval effects, and achieves slightly less power. Using arrays to map introns in yeast, we identified 71% of the previously published introns, detected nine previously undiscovered introns, and observed no false intron discoveries by either method.
References
Benjamini, Y. and Y. Hochberg (1995): “Controlling the false discovery rate: a practical and powerful approach to multiple testin,” J. Royal Statist. Soc., Ser. B., 57, 289–300.Suche in Google Scholar
Buck, M. J., A. B. Nobel and J. D. Lieb (2005): “ChIPOTle: a user-friendly tool for the analysis of ChIP-chip data,” Genome. Biol., 6, R97.Suche in Google Scholar
Cherry, J. M., E. L. Hong, C. Amundsen, R. Balakrishnan, G. Binkley, E. T. Chan, K. R. Christie, M. C. Costanzo, S. S. Dwight, S. R. Engel, D. G. Fisk, J. E. Hirschman, B. C. Hitz, K. Karra, C. J. Krieger, S. R. Miyasato, R. S. Nash, J. Park, M. S. Skrzypek, M. Simison, S. Weng and E. D. Wong (2012): “Saccharomyces Genome Database: the genomics resource of budding yeast,” Nucleic Acids Res., 40, D700–D705.Suche in Google Scholar
David, L., W. Huber, M. Granovskaia, J. Toedling, C. J. Palm, L. Bofkin, T. Jones, R. W. Davis and L. M. Steinmetz (2006): “A high-resolution map of transcription in the yeast genome,” Proc. Natl. Acad. Sci. USA., 103, 5320–5325.Suche in Google Scholar
Genovese, C. R. and L. Wasserman (2006): “Exceedance control of the false discovery proportion,” J. Am. Statist. Assoc., 101, 1408–1417.Suche in Google Scholar
Glaz, J. and N. Balakrishnan (1999): Scan statistics and applications, Birkhäuser, Boston.10.1007/978-1-4612-1578-3Suche in Google Scholar
Guthrie, C. and G. R. Fink (1991): “Guide to yeast genetics and molecular biology,” Methods Enzymol., 194, 1–863.Suche in Google Scholar
Huber, W., J. Toedling and L. M. Steinmetz (2006): “Transcript mapping with high-density oligonucleotide tiling arrays,” Bioinformatics, 22, 1963–1970.10.1093/bioinformatics/btl289Suche in Google Scholar PubMed
Juneau, K., C. Palm, M. Miranda and R. W. Davis (2007): “High-density yeast-tiling array reveals previously undiscovered introns and extensive regulation of meiotic splicing,” Proc. Natl. Acad. Sci. USA., 104, 1522–1527.Suche in Google Scholar
Kechris, K. J., B. Biehs and T. B. Kornberg (2010): “Generalizing moving averages for tiling arrays using combined p-value statistics,” Stat. Appl. Genet. Mol. Biol., 9, Article29.Suche in Google Scholar
Keleş, S., M. J. van der Laan, S. Dudoit and S. E. Cawley (2006): “Multiple testing methods for ChIP-Chip high density oligonucleotide array data,” J. Comput. Biol., 13, 579–613.Suche in Google Scholar
Lin, S., R. Xiao, P. Sun, X. Xu and X. Fu (2005): “Dephosphorylation-dependent sorting of SR splicing factors during mRNP maturation,” Mol. Cell., 20, 413–425.Suche in Google Scholar
Liu, X. S. (2007): “Getting started in tiling microarray analysis,” PLoS Comput. Biol., 3, 1842–1844.Suche in Google Scholar
Perone Pacifico, M., C. Genovese, I. Verdinelli and L. Wasserman (2007): “Scan clustering: a false discovery approach,” J. Multivar. Anal. 98, 1441–1469.Suche in Google Scholar
Perone Pacifico, M. P., C. Genovese, I. Verdinelli and L. Wasserman (2004): “False discovery control for random fields,” J. Am. Statist. Assoc., 99, 1002–1014.Suche in Google Scholar
Primig, M., R. M. Williams, E. A. Winzeler, G. G. Tevzadze, A. R. Conway, S. Y. Hwang, R. W. Davis and R. E. Esposito (2000): “The core meiotic transcriptome in budding yeasts,” Nat. Genet. 26, 415–423.Suche in Google Scholar
Reiner, A., D. Yekutieli and Y. Benjamini (2003): “Identifying differentially expressed genes using false discovery rate controlling procedures,” Bioinformatics, 19, 368–375.10.1093/bioinformatics/btf877Suche in Google Scholar PubMed
Reiner-Benaim, A. (2007): “FDR control by the BH procedure for two-sided correlated tests with implications to gene expression data analysis,” Biometrical. J., 49, 107–126.Suche in Google Scholar
Schwartzman, A., Y. Gavrilov and R. J. Adler (2011): “Multiple testing of local maxima for detection of peaks in 1d,” Ann. Stat., 39, 3290–3319.Suche in Google Scholar
Siegmund, D. O. and B. Yakir (2007): The statistics of gene mapping, Springer, New York.Suche in Google Scholar
Siegmund, D. O., N. R. Zhang and B. Yakir (2011): “False discovery rate for scanning statistics,” Biometrika, 98, 979–985.10.1093/biomet/asr057Suche in Google Scholar
Xu, W., J. Seok, M. N. Mindrinos, A. C. Schweitzer, H. Jiang, J. Wilhelmy, T. A. Clark, K. Kapur, Y. Xing, M. Faham, J. D. Storey, L. L. Moldawer, R. V. Maier, R. G. Tompkins, W. H. Wong, R. W. Davis and W. Xiao (2011): “Human transcriptome array for high-throughput clinical studies,” Proc. Natl. Acad. Sci. USA., 108, 3707–3712.Suche in Google Scholar
Xu, Z., W. Wei, J. Gagneur, F. Perocchi, S. Clauder-Munster, J. Camblong, E. Guffanti, F. Stutz, W. Huber and L. M. Steinmetz (2009): “Bidirectional promoters generate pervasive transcription in yeast,” Nature, 457, 1033–1037.10.1038/nature07728Suche in Google Scholar PubMed PubMed Central
Yazaki, J., B. D. Gregory and J. R. Ecker (2007): “Mapping the genome landscape using tiling array technology,” Curr. Opin. Plant. Biol., 10, 534–542.Suche in Google Scholar
Zhang, Z., J. R. Hesselberth and S. Fields (2007): “Genome-wide identification of spliced introns using a tiling microarray,” Genome. Res., 17, 503–509.Suche in Google Scholar
©2014 by Walter de Gruyter Berlin/Boston
Artikel in diesem Heft
- frontmatter
- Research Articles
- Combining dependent F-tests for robust association of quantitative traits under genetic model uncertainty
- Penalized differential pathway analysis of integrative oncogenomics studies
- A data-smoothing approach to explore and test gene-environment interaction in case-parent trios
- Scan statistics analysis for detection of introns in time-course tiling array data
- Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure
- Improved variational Bayes inference for transcript expression estimation
- Efficient identification of context dependent subgroups of risk from genome-wide association studies
Artikel in diesem Heft
- frontmatter
- Research Articles
- Combining dependent F-tests for robust association of quantitative traits under genetic model uncertainty
- Penalized differential pathway analysis of integrative oncogenomics studies
- A data-smoothing approach to explore and test gene-environment interaction in case-parent trios
- Scan statistics analysis for detection of introns in time-course tiling array data
- Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure
- Improved variational Bayes inference for transcript expression estimation
- Efficient identification of context dependent subgroups of risk from genome-wide association studies
