Hotel Estoril Eden, Monte Estoril,
Portugal
5-8 October 2005

 

 
 

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S
tatistical Issues for Expression Analysis of  Illumina Bead-Based Microarrays

Mark Dunning1, Isabelle Camilier1, Natalie Thorne1 and Simon Tavare1,2
1Department of Oncology, University of Cambridge, UK
2
University of Southern California, USA

Now that the human genome has been sequenced, emphasis is shifting toward the identification and characterization of all the functional elements in the genome.  One approach is first to identify functional elements based on the presence of variability that results in expression level differences. This should identify functionally variable regions that are likely to contribute to complex and quantitative phenotypes and disorders in human populations. The Dermitzakis group at the Sanger Institute is focussing on the identification of allelic variants that modulate the patterns of gene expression in humans. To achieve this, they are associating primary nucleotide variation from the HapMap with gene expression variation.

Expression levels are often monitored using microarrays, typically either two-color spotted arrays or oligo arrays such as those from Affymetrix. The low-level analysis of such arrays has provided some challenging statistical problems. In this talk we discuss the low-level analysis of data obtained from the Illumina expression platform. After introducing the system, we will describe some statistical tools available in R for quality assessment,  background correction and normalization of bead-level data. In the second part of the talk, we describe some of the statistical problems arising from whole-genome scans of high-dimensional phenotypes typified by the Dermitzakis data.