TY - JOUR
T1 - Combining results of microarray experiments
T2 - A rank aggregation approach
AU - DeConde, Robert P.
AU - Hawley, Sarah
AU - Falcon, Seth
AU - Clegg, Nigel
AU - Knudsen, Beatrice
AU - Etzioni, Ruth
N1 - Funding Information:
KEYWORDS: rank aggregation, microarrays, meta-analysis, Markov chains, order-statistic models Author Notes: Research supported by P50 CA 97186 from the National Cancer Institute. Corresponding author: Ruth Etzioni, Fred Hutchinson Cancer Research Center, Mailstop M2-B230, 1100 Fairview Ave North, Seattle, WA 98109-1024. retzioni@fhcrc.org
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Markov chains
KW - Meta-analysis
KW - Microarrays
KW - Order-statistic models
KW - Rank aggregation
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U2 - 10.2202/1544-6115.1204
DO - 10.2202/1544-6115.1204
M3 - Article
C2 - 17049026
AN - SCOPUS:85047234321
SN - 1544-6115
VL - 5
SP - i-23
JO - Statistical Applications in Genetics and Molecular Biology
JF - Statistical Applications in Genetics and Molecular Biology
IS - 1
M1 - 15
ER -