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Activity Number:
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194
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Type:
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Contributed
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Date/Time:
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Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #301472 |
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Title:
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Statistical Methods for Analyzing ChIP-Chip High Density Oligonucleotide Array Data
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Author(s):
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Sunduz Keles*+ and Mark van der Laan and Siew L. Teng and Sandrine Dudoit
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Companies:
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University of California, Berkeley and University of California, Berkeley and University of California, Berkeley and University of California, Berkeley
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Address:
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Division of Biostatistics, Berkeley, CA, 94720-7360,
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Keywords:
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microarrays ; chromatin immunoprecipitation ; multiple testing ; regulatory motif finding ; ChIP on chip data
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Abstract:
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Cawley et al. (2004) recently mapped the locations of binding sites for three transcription factors along human chromosomes 21 and 22 using chromatin immunoprecipitation (IP) of transcription factor bound DNA followed by high density oligonucleotide hybridization of the IP-enriched DNA. This so-called ChIP-Chip technology generates a new type of genomic data for the identification of transcription factor binding sites. We investigate this data structure and propose methods for analyzing it. The proposed methods involve testing for each probe whether it is part of a bound sequence using a scan statistic that takes into account the spatial structure of the data. Different multiple testing procedures are considered for controlling the familywise error rate and false discovery rate. A nested-Bonferroni adjustment that is slightly more powerful than the Bonferroni adjustment when the test statistics are dependent is provided. Simulation studies show that taking into account the spatial structure of the data improves the sensitivity of the multiple testing procedures. Application of the proposed methods to p53 ChIP-Chip data identified many potential target binding regions for p53.
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