BACKGROUND: Microarray gene expression data are commonly perceived as being extremely noisy because of many imperfections inherent in the current technology. A recent study conducted by the MicroArray Quality Control (MAQC) Consortium and published in Nature Biotechnology provides a unique opportunity to probe into the true level of technical noise in such data. RESULTS: In the present report, the MAQC study is reanalyzed in order to quantitatively assess measurement errors inherent in high-density oligonucleotide array technology (Affymetrix platform). The level of noise is directly estimated from technical replicates of gene expression measurements in the absence of biological variability. For each probe set, the magnitude of random fluctuations across technical replicates is characterized by the standard deviation of the corresponding log-expression signal. The resultant standard deviations appear to be uniformly small and symmetrically distributed across probe sets. The observed noise level does not cause any tangible bias in estimated pair-wise correlation coefficients, the latter being particularly prone to its presence in microarray data. CONCLUSION: The reported analysis strongly suggests that, contrary to popular belief, the random fluctuations of gene expression signals caused by technical noise are quite low and the effect of such fluctuations on the results of statistical inference from Affymetrix GeneChip microarray data is negligibly small.

Department of Probability and Statistics Charles University Sokolovska 83 Praha 8 CZ 18675 Czech Republic

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Marshall E. Getting the noise out of gene arrays. Science. 2004;306:630–631. doi: 10.1126/science.306.5696.630. PubMed DOI

Cobb K. Microarrays: The search for meaning in a vast sea of data. Biomed Comput Rev. 2006;2:16–23.

Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY, Lou Y, Sun YA, Willey JC, Setterquist RA, Fischer GM, Tong W, Dragan YP, Dix DJ, Frueh FW, Goodsaid FM, Herman D, Jensen RV, Johnson CD, Lobenhofer EK, Puri RK, Sherf U, Thierry-Mieg J, Wang C, Wilson M, Wolber PK. The microarray quality control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006;24:1151–1161. doi: 10.1038/nbt1239. PubMed DOI PMC

Klebanov L, Yakovlev A. Treating expression levels of different genes as a sample in microarray data analysis: Is it worth a risk? Statist Appl Genet Mol Biol. 2006;5:9. PubMed

Qiu X, Klebanov L, Yakovlev AY. Correlation between gene expression levels and limitations of the empirical Bayes methodology for finding differentially expressed genes. Statist Appl Genet Mol Biol. 2005;4:34. PubMed

Klebanov L, Qiu X, Welle S, Yakovlev A. Statistical methods and microarray data. Nat Biotechnol. 2007;25:25–26. doi: 10.1038/nbt0107-25. PubMed DOI

Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, Behm FG, Raimondi SC, Relling MV, Patel A, Cheng C, Campana D, Wilkins D, Zhou X, Li J, Liu H, Pui CH, Evans WE, Naeve C, Wong L, Downing JR. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell. 2002;1:133–143. doi: 10.1016/S1535-6108(02)00032-6. PubMed DOI

Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WR, Myers RM, Speed TP, Akil H, Watson SJ, Meng F. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucl Acids Res. 2005;33:e175. doi: 10.1093/nar/gni179. PubMed DOI PMC

Qiu X, Brooks AI, Klebanov L, Yakovlev A. The effects of normalization on the correlation structure of microarray data. BMC Bioinformatics. 2005;6:120. doi: 10.1186/1471-2105-6-120. PubMed DOI PMC

Almudevar A, Klebanov LB, Qiu X, Salzman P, Yakovlev AY. Utility of correlation measures in analysis of gene expression. NeuroRx. 2006;3:384–395. doi: 10.1016/j.nurx.2006.05.037. PubMed DOI PMC

Qiu X, Yakovlev A. Some comments on instability of false discovery rate estimation. J Bioinformatics and Comput Biol. 2006;4:1057–1068. doi: 10.1142/S0219720006002338. PubMed DOI

Chen L, Klebanov L, Yakovlev AY. Normality of gene expression revisited. J Biol Syst. 2007.

Lander EC. Array of hope. Nat Genetics. 1999;21:3–4. doi: 10.1038/4427. PubMed DOI

Frantz S. An array of problems. Nat Rev Drug Discov. 2005;4:362–363. doi: 10.1038/nrd1746. PubMed DOI

Klebanov L, Yakovlev A. Is there an alternative to increasing the sample size in microarray studies? Bioinformation. 2007. PubMed PMC

Parmigiani G, Garrett ES, Irizarry RL, Zeger SL. The analysis of gene expression data: An overview of methods and software. In: Parmigiani G, Garrett ES, Irizarry RL, Zeger SL, editor. The Analysis of Gene Expression Data. Springer, New York; 2003.

Zakharkin SO, Kim K, Mehta T, Chen L, Barnes S, Scheirer E, Parrish RS, Allison DB, Page GP. Sources of variation in Affymetrix microarray experiments. BMC Bioinformatics. 2005;6:214. doi: 10.1186/1471-2105-6-214. PubMed DOI PMC

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