JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE B 2006 Vol.7 No.8 P.603-607

http://doi.org/10.1631/jzus.2006.B0603

A robust statistical procedure to discover expression biomarkers using microarray genomic expression data

Author(s): ZOU Yang-yun, YANG Jian, ZHU Jun
Affiliation(s): Institute of Bioinformatics, Zhejiang University, Hangzhou 310029, China
Corresponding email(s): jzhu@zju.edu.cn
Key Words: Microarray, Biomarker, Henderson method III, Gene expression pattern, Mixed linear model

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Abstract: microarray has become increasingly popular biotechnology in biological and medical researches, and has been widely applied in classification of treatment subtypes using expression patterns of biomarkers. We developed a statistical procedure to identify expression biomarkers for treatment subtype classification by constructing an F-statistic based on henderson method III. Monte Carlo simulations were conducted to examine the robustness and efficiency of the proposed method. Simulation results showed that our method could provide satisfying power of identifying differentially expressed genes (DEGs) with false discovery rate (FDR) lower than the given type I error rate. In addition, we analyzed a leukemia dataset collected from 38 leukemia patients with 27 samples diagnosed as acute lymphoblastic leukemia (ALL) and 11 samples as acute myeloid leukemia (AML). We compared our results with those from the methods of significance analysis of microarray (SAM) and microarray analysis of variance (MAANOVA). Among these three methods, only expression biomarkers identified by our method can precisely identify the three human acute leukemia subtypes.

Reference

[1] Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., et al., 2000. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403(6769):503-511.

[2] Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1):289-300.

[3] Fowlkes, E.B., Mallows, C.L., 1983. A method for comparing two hierarchical clusterings. J. American Statistical Association, 78(383):553-569.

[4] Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., et al., 1999. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286(5439):531-537.

[5] Hubert, L., Arabie, P., 1985. Comparing partitions. Journal of Classification, 2(1):193-218.

[6] Jain, A.K., Dubes, R.C., 1988. Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ.

[7] Jin, W., Riley, R.M., Wolfinger, R.D., White, K.P., Passador-Gurgel, G., Gibson, G., 2001. The contributions of sex, genotype and age to transcriptional variance in drosophila melanogaster. Nature Genetics, 29(4):389-395.

[8] Kerr, M.K., Churchill, G.A., 2001. Experimental design for gene expression microarrays. Biostatistics, 2(2):183-201.

[9] Kerr, M.K., Martin, M., Churchill, G.A., 2000. Analysis of variance for gene expression microarray data. Journal of Computational Biology, 7(6):819-837.

[10] Lu, Y., Zhu, J., Liu, P., 2005. A two-step strategy for detecting differential gene expression of cDNA microarray data. Current Genetics, 47(2):121-131.

[11] Searle, S.R., 1971. Linear Models. John Wiley & Sons, New York.

[12] Spindler, S.R., 2006. Use of microarray biomarkers to identify longevity therapeutics. Aging Cell, 5(1):39-50.

[13] Tusher, V.G., Tibshirani, R., Chu, G., 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Nat. Acad. Sci. USA, 98(9):5116-5121.

[14] Wang, C.S., Rutledge, J.J., Gianola, D., 1994. Bayesian analysis of mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs. Genetics Selection Evolution, 26:91-115.

[15] Wolfinger, R.D., Gibson, G., Wolfinger, E.D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., Paules, R.S., 2001. Assessing gene significance from cDNA microarray expression data via mixed models. Journal of Computational Biology, 8(6):625-638.

[16] Woo, Y., Krueger, W., Kaur, A., Churchill, G., 2005. Experimental design for three-color and four-color gene expression microarrays. Bioinformatics, 21(Suppl. 1):i459-i467.

[17] Zhu, J., 2000. Mixed linear model approaches for analyzing genetic models of complex quantitative traits. Journal of Zhejiang University SCIENCE, 1(1):78-90.

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