Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 3, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa163
Keywords
NanoString nCounter expression; gene expression normalization; quality control; data visualization
Funding
- Susan G. Komen
- National Institutes of Health, National Cancer Institute [P01-CA151135, P50-CA05822, U01-CA179715]
- National Institute of General Medical Sciences [1T32GM12274]
- National Cancer Institute [3P30CA016086]
- University of North Carolina at Chapel Hill University Cancer Research Fund
- Intramural Research Program of the National Institutes of Health
- National Cancer Institute
- [P01-CA142538]
- [P30-ES010126]
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The NanoString RNA counting assay is sensitive and reproducible for formalin-fixed paraffin embedded samples. A comprehensive normalization procedure developed here eliminates technical variation more reliably without diminishing biological variation, emphasizing the importance of systematic quality control, normalization, and visualization of NanoString nCounter data.
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 = 1649) from the Carolina Breast Cancer Study, two cohorts of moderate sample size (N = 359 and130) 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.
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