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Jia G, Wang X, Li Q, Lu W, Tang X, Wistuba I, Xie Y. RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data. Ann Appl Stat 2019; 13:1617-1647. [PMID: 33564347 PMCID: PMC7869841 DOI: 10.1214/19-aoas1249] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
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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Affiliation(s)
- Gaoxiang Jia
- Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, P O Box 750332, Dallas, Texas 75275
- Quantitative Biomedical Research Center, Department of Clinical Sciences, The University of Texas Southwestern Medical Center, Dallas, Texas 75390
| | - Xinlei Wang
- Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, P O Box 750332, Dallas, Texas 75275
| | - Qiwei Li
- Quantitative Biomedical Research Center, Department of Clinical Sciences, The University of Texas Southwestern Medical Center, Dallas, Texas 75390
| | - Wei Lu
- Department of Translational Molecular Pathology, University of Texas, MD Anderson Cancer Center, Houston, Texas 77030
| | - Ximing Tang
- Department of Translational Molecular Pathology, University of Texas, MD Anderson Cancer Center, Houston, Texas 77030
| | - Ignacio Wistuba
- Department of Translational Molecular Pathology, University of Texas, MD Anderson Cancer Center, Houston, Texas 77030
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Clinical Sciences, The University of Texas Southwestern Medical Center, Dallas, Texas 75390
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