An approach for normalization and quality control for NanoString RNA expression data

Abstract

The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString’s commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.

Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer

“…Pearson distance measures and complete linkage were used to determine clusters. Java TreeView was used to visualize the clustered data and generate heatmaps (11). The compareGroups R package was used to generate descriptive tables and to determine the statistical significance of associations among variables (11).…”

“…RNA counting methods, such as nCounter (NanoString Technologies, Inc), that do not require enzymatic reactions allow for FFPE samples to be used in expression profiling (11), but the feasibility and value of these approaches have not been robustly assessed in NMIBC. Thus, we sought to evaluate gene expression signatures determined by RNA counting to identify associations with clinicopathologic characteristics, assess prognostic and predictive significance, and evaluate the added value of gene expression data over established DNA mutation sequencing methods in NMIBC.…”

Gene-level germline contributions to clinical risk of recurrence scores in Black and White breast cancer patients

“…Paraffin-embedded tumor blocks were assayed for gene expression of 406 BC-related and 11 housekeeping genes using NanoString nCounter at the Translational Genomics Laboratory at UNC-Chapel Hill [4, 9]. As described previously, we eliminated samples with insufficient data quality using NanoStringQCPro [16, 33], scaled distributional difference between lanes with upper-quartile normalization [34], and removed two dimensions of unwanted technical and biological variation, estimated from housekeeping genes using RUVSeq [34, 35]. The current analysis included 1,199 samples with both genotype and gene expression data (628 BW, 571 WW).…”

Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy

“…Absolute counts were quantified by the nCounter digital analyzer (NanoString Technologies, nCounter MAX Analysis System, Seatle, United States of America). Raw counts were normalized using the RUVSeq method adjusted for nanoString nCounter ® gene expression analysis as described by Bhattacharya et al [ 36 ]. Principal component analysis was performed using basic R function “prcomp.”…”