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.

中文翻译：

---

NanoString RNA 表达数据的标准化和质量控制方法。

用于福尔马林固定石蜡包埋样品的 NanoString RNA 计数测定在临床和档案样品分析方面具有独特的灵敏度、技术重现性和稳健性。虽然 NanoString 提供了商业标准化方法，但它们并不是对所有设置都是最佳的，特别是当样本表现出强烈的技术或生物学变异或管家基因在整个队列中具有不同的性能时。在这里，我们开发和评估了更全面的 NanoString 数据标准化程序，包括质量控制、内务目标选择、标准化和迭代数据可视化以及生物验证步骤。该方法使用来自卡罗莱纳州乳腺癌研究的大型队列（⁠|$N=\kern0.5em 1649$|⁠ ）进行了评估，两个样本大小适中的队列（ ⁠|$N=359$|和|$130$| ） ⁠ ) 和一个小型已发布数据集 ( ⁠|$N=12$|⁠ )。这里开发的迭代过程比其他三种方法（包括 NanoString 的商业包）更可靠地消除了技术变化（例如来自不同研究阶段或地点），而不会减少生物变化，特别是在长期纵向多阶段或多地点队列中。我们还发现针对 nCounter 验证的探针组（例如 PAM50 基因签名）不受批次问题的影响。这项工作强调 NanoString nCounter 数据的系统质量控制、标准化和可视化是影响下游分析结果的研究设计的重要组成部分。