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Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression

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  • Bin Wang
  • Shu-Guang Zhang
  • Xiao-Feng Wang
  • Ming Tan
  • Yaguang Xi

Abstract

MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases. The total number of microRNAs in humans is approximately 1,800, which challenges some analytical methodologies requiring a large number of entries. Unlike messenger RNA, the majority of microRNA (60%) maintains relatively low abundance in the cells. When analyzed using microarray, the signals of these low-expressed microRNAs are influenced by other non-specific signals including the background noise. It is crucial to distinguish the true microRNA signals from measurement errors in microRNA array data analysis. In this study, we propose a novel measurement error model-based normalization method and differentially-expressed microRNA detection method for microRNA profiling data acquired from locked nucleic acids (LNA) microRNA array. Compared with some existing methods, the proposed method significantly improves the detection among low-expressed microRNAs when assessed by quantitative real-time PCR assay.

Suggested Citation

  • Bin Wang & Shu-Guang Zhang & Xiao-Feng Wang & Ming Tan & Yaguang Xi, 2012. "Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-12, May.
    • Handle: RePEc:plo:pone00:0037537
    DOI: 10.1371/journal.pone.0037537
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    References listed on IDEAS

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    1. Rao Youlan & Lee Yoonkyung & Jarjoura David & Ruppert Amy S & Liu Chang-gong & Hsu Jason C & Hagan John P, 2008. "A Comparison of Normalization Techniques for MicroRNA Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-20, July.
    2. Wang, Xiao-Feng & Wang, Bin, 2011. "Deconvolution Estimation in Measurement Error Models: The R Package decon," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i10).
    3. Bin Wang & Paul Howel & Skjalg Bruheim & Jingfang Ju & Laurie B Owen & Oystein Fodstad & Yaguang Xi, 2011. "Systematic Evaluation of Three microRNA Profiling Platforms: Microarray, Beads Array, and Quantitative Real-Time PCR Array," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-12, February.
    4. Wang, Xiao-Feng & Fan, Zhaozhi & Wang, Bin, 2010. "Estimating smooth distribution function in the presence of heteroscedastic measurement errors," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 25-36, January.
    5. Delaigle, Aurore & Fan, Jianqing & Carroll, Raymond J., 2009. "A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 348-359.
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