Combining Results of Microarray Experiments: A Rank Aggregation Approach

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.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  M. Fligner,et al.  Multistage Ranking Models , 1988 .

[3]  Cornelia I Bargmann,et al.  Comparing genomic expression patterns across species identifies shared transcriptional profile in aging , 2004, Nature Genetics.

[4]  R. Plackett.  The Analysis of Permutations , 1975 .

[5]  J. Nelson,et al.  Increased fatty acid synthase as a therapeutic target in androgen‐independent prostate cancer progression , 2001, The Prostate.

[6]  J. Welsh,et al.  Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. , 2001, Cancer research.

[7]  S. Sealfon,et al.  Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. , 2002, Nucleic acids research.

[8]  Ronald Fagin,et al.  Comparing top k lists , 2003, SODA '03.

[9]  John T. Wei,et al.  Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. , 2005, Cancer cell.

[10]  S. Dhanasekaran,et al.  Delineation of prognostic biomarkers in prostate cancer , 2001, Nature.

[11]  R. A. Bradley,et al.  RANK ANALYSIS OF INCOMPLETE BLOCK DESIGNS , 1952 .

[12]  M. Lerman,et al.  STRA13 expression and subcellular localisation in normal and tumour tissues: implications for use as a diagnostic and differentiation marker , 2005, Journal of Medical Genetics.

[13]  M. Dean Chamberlain,et al.  The Smaller Isoforms of Ankyrin 3 Bind to the p85 Subunit of Phosphatidylinositol 3′-Kinase and Enhance Platelet-derived Growth Factor Receptor Down-regulation* , 2006, Journal of Biological Chemistry.

[14]  Moni Naor,et al.  Rank aggregation methods for the Web , 2001, WWW '01.

[15]  L. Thurstone.  Rank order as a psycho-physical method. , 1931 .

[16]  P. Brown,et al.  Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Clement Ip,et al.  Androgen receptor signaling intensity is a key factor in determining the sensitivity of prostate cancer cells to selenium inhibition of growth and cancer-specific biomarkers , 2005, Molecular Cancer Therapeutics.

[18]  T. Barrette,et al.  α-Methylacyl-CoA Racemase: Expression Levels of this Novel Cancer Biomarker Depend on Tumor Differentiation , 2002 .

[19]  J. Marden.  Analyzing and Modeling Rank Data , 1996 .

[20]  Leroy Hood,et al.  A molecular correlate to the Gleason grading system for prostate adenocarcinoma. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[21]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[22]  Albert Maydeu-Olivares,et al.  Thurstonian modeling of ranking data via mean and covariance structure analysis , 1999 .

[23]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[24]  T. Barrette,et al.  Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. , 2002, Cancer research.

[25]  M. Bittner,et al.  Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. , 2001, Cancer research.

[26]  Kevin R. Coombes,et al.  Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies , 2004, Bioinform..

[27]  Giovanni Parmigiani,et al.  A Cross-Study Comparison of Gene Expression Studies for the Molecular Classification of Lung Cancer , 2004, Clinical Cancer Research.

[28]  O. Klezovitch,et al.  Hepsin promotes prostate cancer progression and metastasis. , 2004, Cancer cell.