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

Abstract

<jats:title>ABSTRACT</jats:title><jats:p>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 = 1,649) 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 multi-phase or multi-site 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 is an imperative component of study design that influences results in downstream analyses.</jats:p>

Assessing Tumor-Infiltrating Lymphocytes in Breast Cancer: A Proposal for Combining Immunohistochemistry and Gene Expression Analysis to Refine Scoring

“…Normalization of raw RNA counts using nSolver software was not satisfactory (Figure 3A). Therefore, we used an alternative approach by normalizing raw counts using the RUVSeq package (30). TIL scores were calculated using normalized counts from the nSolver and RUVSeq analysis and the cell type profiling algorithm described by Danaher et al (24).…”

TP53 Pathway Function, Estrogen Receptor Status, and Breast Cancer Risk Factors in the Carolina Breast Cancer Study

“…RNA expression in CBCS has been quantified using NanoString assays on FFPE tumor samples, with tumor tissue slides and cores used for RNA isolation in Phase 1-2 and Phase 3, respectively. 31,34,35 A previously validated RNA signature (Table S1) that aggregates information on TP53-dependent genes was used to classify TP53 functional status (mutant-like or wildtype-like) based on a similarity-to-centroid approach. 26 RNA-based TP53 status was missing for the majority of cases in Phase 1 (70%) and for about half of cases in Phases 2 and 3 (48% and 53%, respectively).…”

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

“…Paraffin-embedded tumor blocks were requested from participating pathology laboratories for each sample, were reviewed, and were assayed for gene expression using the NanoString nCounter system, as discussed previously ( 43 ). As described before ( 10 , 63 ), the expression data (406 genes and 11 housekeeping genes) were pre-processed and normalized using quality control steps from the NanoStringQCPro package, median ratio normalization using DESeq2 ( 57 , 64 ), and estimation and removal of unwanted technical variation using the RUVSeq and limma packages ( 65 , 66 ). The resulting normalized dataset comprised of samples from 1199 patients, comprising of 628 women of African descent (AA) and 571 women of European descent (EA).…”