Open Access
September 2019 RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data
Gaoxiang Jia, Xinlei Wang, Qiwei Li, Wei Lu, Ximing Tang, Ignacio Wistuba, Yang Xie
Ann. Appl. Stat. 13(3): 1617-1647 (September 2019). DOI: 10.1214/19-AOAS1249


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.


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Gaoxiang Jia. Xinlei Wang. Qiwei Li. Wei Lu. Ximing Tang. Ignacio Wistuba. Yang Xie. "RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data." Ann. Appl. Stat. 13 (3) 1617 - 1647, September 2019.


Received: 1 March 2018; Revised: 1 November 2018; Published: September 2019
First available in Project Euclid: 17 October 2019

zbMATH: 07145970
MathSciNet: MR4019152
Digital Object Identifier: 10.1214/19-AOAS1249

Keywords: Bayesian hierarchical modeling , control probes , FFPE , housekeeping gene , normalization , random coefficients regression

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.13 • No. 3 • September 2019
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