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Cited by 122Gene expression and cancer classificationMolecular Biology Techniques and ApplicationsSingle-cell and spatial transcriptomics

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

Open access
Bhattacharya, Arjun, Hamilton, Alina M, Furberg, Helena, Pietzak, Eugene, Purdue, Mark P, Troester, Melissa A, Hoadley, Katherine A, Love, Michael I·2021·Briefings in Bioinformatics
DOI 10.1093/bib/bbaa163PMC PMC8138885

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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.

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  • Quality control was performed using RUV_total to generate W1 for correcting unwanted sources of sample-level variance.

    “Quality control was performed using the RUV_total function from the NanoNormIter R package to generate a continuous metadata value (“W1”) for each sample, which was used for correcting unwanted sources of sample-level variance (51).”

    From Multi-omics analyses reveal novel effects of PLCγ2 deficiency in the mouse brain (2023) · cites this paper as [51]

    The Normalization section says the paper estimated unwanted technical factors using RUVg from RUVSeq and removed estimated variation using limma’s removeBatchEffects. This supports the general purpose of correcting unwanted variation, but not the attributed use of RUV_total from NanoNormIter or generation of a per-sample value called W1. Page numbers were not available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

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Authors
Bhattacharya, A., Hamilton, A. M., Furberg, H., Pietzak, E., Purdue, M. P., Troester, M. A., Hoadley, K. A., & Love, M. I.
Affiliations
  • University of North Carolina at Chapel Hill (US)
  • Memorial Sloan Kettering Cancer Center (US)
  • National Cancer Institute (US)
Corresponding author
  • Hoadley, Katherine A
  • Love, Michael I
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2020-08-13
Volume
22
Issue
3
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbaa163
PMC
PMC8138885
OpenAlex
W3049216986
Cited by
122
References
4
Citations by year
2026: 20 · 2025: 22 · 2024: 23 · 2023: 27 · 2022: 18 · 2021: 10 · 2020: 2
FWCI
4.62
Type
journal article
Language
en
Open access
Licence
Locations
  • Briefings in Bioinformatics (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
  • UNC Libraries
Funders
  • National Institutes of Health
  • National Cancer Institute (P01-CA151135, P50-CA05822, U01-CA179715, P01-CA142538, P30-ES010126, 3P30CA016086)
  • Komen Career Catalyst (CCR16376756)
  • National Institute of General Medical Sciences (1T32GM12274)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
NanoString nCounter, quality control, housekeeping genes, formalin-fixed paraffin-embedded tissue, technical variability, batch effects, breast cancer, data visualization
MeSH terms
Breast Neoplasms, Female, Humans, RNA, RNA, Neoplasm, Gene Expression Regulation, Neoplastic, Gene Expression Profiling, Databases, Nucleic Acid

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