  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.
