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The accurate quantification of gene expression levels is crucial fortranscriptome study. Microarray platforms are commonly used for simultaneouslyinterrogating thousands of genes in the past decade, and recently RNA-Seq hasemerged as a promising alternative. The gene expression measurements obtainedby microarray and RNA-Seq are, however, subject to various measurement errors.A third platform called qRT-PCR is acknowledged to provide more accuratequantification of gene expression levels than microarray and RNA-Seq, but ithas limited throughput capacity. In this article, we propose to use a system offunctional measurement error models to model gene expression measurements andcalibrate the microarray and RNA-Seq platforms with qRT-PCR. Based on thesystem, a two-step approach was developed to estimate the biases and errorvariance components of the three platforms and calculate calibrated estimatesof gene expression levels. The estimated biases and variance components shedlight on the relative strengths and weaknesses of the three platforms and thecalibrated estimates provide a more accurate and consistent quantification ofgene expression levels. Theoretical and simulation studies were conducted toestablish the properties of those estimates. The system was applied to analyzetwo gene expression data sets from the Microarray Quality Control (MAQC) andSequencing Quality Control (SEQC) projects.

Statistical calibration of qRT-PCR, microarray and RNA-Seq gene expression data with measurement error models