  Formalin-fixed paraffin-embedded (FFPE) samples have great potential for
biomarker discovery, retrospective studies and diagnosis or prognosis of
diseases. Their application, however, is hindered by the unsatisfactory
performance of traditional gene expression profiling techniques on damaged
RNAs. NanoString nCounter platform is well suited for profiling of FFPE samples
and measures gene expression with high sensitivity which may greatly facilitate
realization of scientific and clinical values of FFPE samples. However,
methodological development for normalization, a critical step when analyzing
this type of data, is far behind. Existing methods designed for the platform
use information from different types of internal controls separately and rely
on an overly-simplified assumption that expression of housekeeping genes is
constant across samples for global scaling. Thus, these methods are not
optimized for the nCounter system, not mentioning that they were not developed
for FFPE samples. We construct an integrated system of random-coefficient
hierarchical regression models to capture main patterns and characteristics
observed from NanoString data of FFPE samples and develop a Bayesian approach
to estimate parameters and normalize gene expression across samples. Our
method, labeled RCRnorm, incorporates information from all aspects of the
experimental design and simultaneously removes biases from various sources. It
eliminates the unrealistic assumption on housekeeping genes and offers great
interpretability. Furthermore, it is applicable to freshly frozen or like
samples that can be generally viewed as a reduced case of FFPE samples.
Simulation and applications showed the superior performance of RCRnorm.
