Incorporation of Subject-level Covariates in Quantile Normalization of MiRNA Data

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2015 Dec 15

PMID 26653287

Citations 5

Authors

Anvar Suyundikov

John R Stevens

Christopher Corcoran

Jennifer Herrick

Roger K Wolff

Martha L Slattery

Affiliations

Soon will be listed here.

Abstract

Background: Most currently-used normalization methods for miRNA array data are based on methods developed for mRNA arrays despite fundamental differences between the data characteristics. The application of conventional quantile normalization can mask important expression differences by ignoring demographic and environmental factors. We present a generalization of the conventional quantile normalization method, making use of available subject-level covariates in a colorectal cancer study.

Results: In simulation, our weighted quantile normalization method is shown to increase statistical power by as much as 10 % when relevant subject-level covariates are available. In application to the colorectal cancer study, this increase in power is also observed, and previously-reported dysregulated miRNAs are rediscovered.

Conclusions: When any subject-level covariates are available, the weighted quantile normalization method should be used over the conventional quantile normalization method.

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References

Cunningham D, Atkin W, Lenz H, Lynch H, Minsky B, Nordlinger B . Colorectal cancer. Lancet. 2010; 375(9719):1030-47. DOI: 10.1016/S0140-6736(10)60353-4. View

Pradervand S, Weber J, Thomas J, Bueno M, Wirapati P, Lefort K . Impact of normalization on miRNA microarray expression profiling. RNA. 2009; 15(3):493-501. PMC: 2657010. DOI: 10.1261/rna.1295509. View

Weber M, Banks E, Ward R, Sitas F . Population characteristics related to colorectal cancer testing in New South Wales, Australia: results from the 45 and Up Study cohort. J Med Screen. 2008; 15(3):137-42. DOI: 10.1258/jms.2008.008050. View

Hawk E, Levin B . Colorectal cancer prevention. J Clin Oncol. 2005; 23(2):378-91. DOI: 10.1200/JCO.2005.08.097. View

Garmire L, Subramaniam S . Evaluation of normalization methods in mammalian microRNA-Seq data. RNA. 2012; 18(6):1279-88. PMC: 3358649. DOI: 10.1261/rna.030916.111. View

Wang B, Xi Y . Challenges for MicroRNA Microarray Data Analysis. Microarrays (Basel). 2013; 2(2). PMC: 3807239. DOI: 10.3390/microarrays2020034. View

Suo C, Salim A, Chia K, Pawitan Y, Calza S . Modified least-variant set normalization for miRNA microarray. RNA. 2010; 16(12):2293-303. PMC: 2995391. DOI: 10.1261/rna.2345710. View

Deo A, Carlsson J, Lindlof A . How to choose a normalization strategy for miRNA quantitative real-time (qPCR) arrays. J Bioinform Comput Biol. 2011; 9(6):795-812. DOI: 10.1142/s0219720011005793. View

Lopez-Romero P, Gonzalez M, Callejas S, Dopazo A, Irizarry R . Processing of Agilent microRNA array data. BMC Res Notes. 2010; 3:18. PMC: 2823597. DOI: 10.1186/1756-0500-3-18. View

10.

Mazeh H, Mizrahi I, Ilyayev N, Halle D, Brucher B, Bilchik A . The Diagnostic and Prognostic Role of microRNA in Colorectal Cancer - a Comprehensive review. J Cancer. 2013; 4(3):281-95. PMC: 3584841. DOI: 10.7150/jca.5836. View

11.

Dong Y, Wu W, Wu C, Sung J, Yu J, Ng S . MicroRNA dysregulation in colorectal cancer: a clinical perspective. Br J Cancer. 2011; 104(6):893-8. PMC: 3065287. DOI: 10.1038/bjc.2011.57. View

12.

Bolstad B, Irizarry R, Astrand M, Speed T . A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003; 19(2):185-93. DOI: 10.1093/bioinformatics/19.2.185. View

13.

Stevens J, Nicholas G . Assessing numerical dependence in gene expression summaries with the jackknife expression difference. PLoS One. 2012; 7(8):e39570. PMC: 3411624. DOI: 10.1371/journal.pone.0039570. View

14.

Zhao Y, Wang E, Liu H, Rotunno M, Koshiol J, Marincola F . Evaluation of normalization methods for two-channel microRNA microarrays. J Transl Med. 2010; 8:69. PMC: 2917409. DOI: 10.1186/1479-5876-8-69. View

15.

Watson A, Collins P . Colon cancer: a civilization disorder. Dig Dis. 2011; 29(2):222-8. DOI: 10.1159/000323926. View

16.

Kang J, Xu E . An integrated hierarchical Bayesian approach to normalizing left-censored microRNA microarray data. BMC Genomics. 2013; 14:507. PMC: 3734108. DOI: 10.1186/1471-2164-14-507. View

17.

Stevens J, Bell J, Aston K, White K . A comparison of probe-level and probeset models for small-sample gene expression data. BMC Bioinformatics. 2010; 11:281. PMC: 2901368. DOI: 10.1186/1471-2105-11-281. View

18.

Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F . A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009; 10(6):R64. PMC: 2718498. DOI: 10.1186/gb-2009-10-6-r64. View

19.

Meyer S, Pfaffl M, Ulbrich S . Normalization strategies for microRNA profiling experiments: a 'normal' way to a hidden layer of complexity?. Biotechnol Lett. 2010; 32(12):1777-88. DOI: 10.1007/s10529-010-0380-z. View

20.

Bargaje R, Hariharan M, Scaria V, Pillai B . Consensus miRNA expression profiles derived from interplatform normalization of microarray data. RNA. 2009; 16(1):16-25. PMC: 2802026. DOI: 10.1261/rna.1688110. View