ArticleBriefings in bioinformatics2021
An approach for normalization and quality control for NanoString RNA expression data.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
98 citing papers in PubMed, 1 synthesis or guideline pooled it, 115 citations in OpenAlex.
- MetaNorm: incorporating meta-analytic priors into normalization of NanoString nCounter data.Bioinformatics (Oxford, England) · 2024Pooled it
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- Molecular characterization of colorectal cancer liver metastases with pure desmoplastic versus non-pure desmoplastic histopathological growth patterns using a targeted gene expression panel.Clinical & experimental metastasis · 2026Article
- Normalization of nCounter Gene Expression Data Alters Molecular Diagnostics in Kidney Transplantation.Journal of the American Society of Nephrology : JASN · 2026Article
- Authors' Reply: Normalization Benchmarking for nCounter Diagnostics Requires Deployment- and Biology-Aware Evaluation.Journal of the American Society of Nephrology : JASN · 2026Article
- Normalization Benchmarking for nCounter Diagnostics Requires Deployment- and Biology-Aware Evaluation.Journal of the American Society of Nephrology : JASN · 2026Article
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- Spatial and bulk transcriptomics reveal distinct molecular signatures in Kaposi sarcoma with and without other KSHV-associated diseases.Journal of translational medicine · 2026Article
- Molecular and socioeconomic characteristics of inflammatory breast cancer in the Carolina Breast Cancer Study.Breast cancer research and treatment · 2026Article
- Preoperative positive ctDNA analysis is associated with the tumor microenvironment, and the risk of recurrence in non-metastatic colorectal cancer.NPJ precision oncology · 2026Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.