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June 2014 Statistical calibration of qRT-PCR, microarray and RNA-Seq gene expression data with measurement error models
Zhaonan Sun, Thomas Kuczek, Yu Zhu
Ann. Appl. Stat. 8(2): 1022-1044 (June 2014). DOI: 10.1214/14-AOAS721


The accurate quantification of gene expression levels is crucial for transcriptome study. Microarray platforms are commonly used for simultaneously interrogating thousands of genes in the past decade, and recently RNA-Seq has emerged as a promising alternative. The gene expression measurements obtained by microarray and RNA-Seq are, however, subject to various measurement errors. A third platform called qRT-PCR is acknowledged to provide more accurate quantification of gene expression levels than microarray and RNA-Seq, but it has limited throughput capacity. In this article, we propose to use a system of functional measurement error models to model gene expression measurements and calibrate the microarray and RNA-Seq platforms with qRT-PCR. Based on the system, a two-step approach was developed to estimate the biases and error variance components of the three platforms and calculate calibrated estimates of gene expression levels. The estimated biases and variance components shed light on the relative strengths and weaknesses of the three platforms and the calibrated estimates provide a more accurate and consistent quantification of gene expression levels. Theoretical and simulation studies were conducted to establish the properties of those estimates. The system was applied to analyze two gene expression data sets from the Microarray Quality Control (MAQC) and Sequencing Quality Control (SEQC) projects.


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Zhaonan Sun. Thomas Kuczek. Yu Zhu. "Statistical calibration of qRT-PCR, microarray and RNA-Seq gene expression data with measurement error models." Ann. Appl. Stat. 8 (2) 1022 - 1044, June 2014.


Published: June 2014
First available in Project Euclid: 1 July 2014

zbMATH: 06333786
MathSciNet: MR3262544
Digital Object Identifier: 10.1214/14-AOAS721

Keywords: comparative calibration , functional and structural parameters , gene differential expression , transcriptome profiling

Rights: Copyright © 2014 Institute of Mathematical Statistics


Vol.8 • No. 2 • June 2014
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