RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data

TL;DR

RCRnorm is a Bayesian hierarchical model that improves normalization of NanoString nCounter data from FFPE samples by integrating multiple sources of information, outperforming existing methods.

Contribution

It introduces a novel Bayesian hierarchical normalization method tailored for NanoString FFPE data, addressing limitations of previous approaches.

Findings

RCRnorm outperforms existing normalization methods in simulations.

It effectively removes biases from various sources in FFPE data.

The method is applicable to both FFPE and frozen samples.

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.