Combining Results of Microarray Experiments: A Rank Aggregation Approach
DeConde Robert P,
Knudsen Beatrice and
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DeConde Robert P: Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, WA
Hawley Sarah: Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, WA
Falcon Seth: Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA
Clegg Nigel: Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA
Knudsen Beatrice: Fred Hutchinson Cancer Research Center
Etzioni Ruth: Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, WA
Statistical Applications in Genetics and Molecular Biology, 2006, vol. 5, issue 1, 1-25
As technology for microarray analysis becomes widespread, it is becoming increasingly important to be able to compare and combine the results of experiments that explore the same scientific question. In this article, we present a rank-aggregation approach for combining results from several microarray studies. The motivation for this approach is twofold; first, the final results of microarray studies are typically expressed as lists of genes, rank-ordered by a measure of the strength of evidence that they are functionally involved in the disease process, and second, using the information on this rank-ordered metric means that we do not have to concern ourselves with data on the actual expression levels, which may not be comparable across experiments. Our approach draws on methods for combining top-k lists from the computer science literature on meta-search. The meta-search problem shares several important features with that of combining microarray experiments, including the fact that there are typically few lists with many elements and the elements may not be common to all lists. We implement two meta-search algorithms, which use a Markov chain framework to convert pairwise preferences between list elements into a stationary distribution that represents an aggregate ranking (Dwork et al, 2001). We explore the behavior of the algorithms in hypothetical examples and a simulated dataset and compare their performance with that of an algorithm based on the order-statistics model of Thurstone (Thurstone, 1927). We apply all three algorithms to aggregate the results of five microarray studies of prostate cancer.
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