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Statistical Issues in the Analysis of ChIP-chip High Density Oligonucleotide Array Data

Sunduz Keles, PhD,
Dept. of Biostatistics and Medical Informatics, UW-Madison

October 22, 2004, 12 - 1 pm in room 1210 Medical Sciences Center, 1300 University Ave.
PLEASE NOTE LOCATION...THIS WAS IMPROPERLY LISTED ON THIS SITE EARLIER

ABSTRACT

Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription factors along human chromosomes 21 and 22 using ChIP-Chip experiments. ChIP-Chip experiments are a new approach to the genome-wide identification of transcription factor binding sites and consist of chromatin (Ch) immunoprecipitation (IP) of transcription factor-bound genomic DNA followed by high density oligonucleotide hybridization (Chip) of the IP-enriched DNA.

We investigate the ChIP-Chip data structure and propose methods for inferring the location of transcription factor binding sites from these data. The proposed methods involve testing for each probe whether it is part of a bound sequence or not using a scan statistic that takes into account the spatial structure of the data. Different multiple testing procedures are considered for controlling the family-wise error rate and false discovery rate. A nested-Bonferroni adjustment, that is more powerful than the traditional Bonferroni adjustment when the test statistics are dependent, is discussed. Simulation studies show that taking into account the spatial structure of the data substantially improves the sensitivity of the multiple testing procedures. Application of the proposed methods to ChIP-Chip data for transcription factor p53 identified many potential target binding regions along human chromosomes 21 and 22.

Joint work with Mark J. van der Laan (UC Berkeley), Sandrine Dudoit (UC Berkeley) and Simon E. Cawley (Affymetrix).

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