Background modeling, Quality Control and Normalization for GeoMx RNA data with GeoDiff
Background modeling, Quality Control and Normalization for GeoMx RNA data with GeoDiff
复制标题
使用 GeoDiff 对 GeoMx RNA 数据进行背景建模、质量控制和标准化
DOI：
10.1101/2022.05.26.493637
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
J. Beechem
中科院分区：
文献类型：
--
作者：
Lei Yang;Zhi Yang;P. Danaher;Stephanie Zimmerman;Tyler D. Hether;David Henderson;J. Beechem
NanoString的GeoMx Digital Spatial Profiler（DSP）RNA分析可以测量数百个可定制形状和大小的区域的mRNA，但由于非特异性探针结合引起的无所不在的背景噪声，它在质量控制（QC）和标准化方面带来了独特的挑战，这是传统方法无法解决的。结果和讨论使用Poisson背景模型、背景分数检验、负二项阈值模型和Poisson阈值模型从R包GeoDiff进行归一化，我们对GoeMx RNA检测数据执行包括尺寸因子估计、QC和归一化的任务。结果表明，它们在一致性/假阳性率方面优于传统方法，如QC定量限，在消除技术变异性和恢复真实信号方面优于75%分位数归一化。结论我们提出了一种基于统计模型的QC工作流程，并使用GeoDiff标准化GeoMx RNA数据，通过统计理论证明并通过真实的/模拟数据进行验证。
Background NanoString’s GeoMx Digital Spatial Profiler (DSP) RNA assay can measure mRNA from hundreds of regions of customizable shape and size, yet it gives unique challenge in Quality Control(QC) and normalizating due to the omnipresent background noise incurred by the non-specific probe binding, which could not be addressed by conventional methods. Results and discussion Using Poisson Background model, Background Score Test, Negative Binomial threshold model and Poisson threshold model for normalization from the R package GeoDiff, we perform tasks including size factor estimation, QC and normalization on GoeMx RNA assay data. They are shown to outperform conventional methods like Limit of Quantification for QC as to consistency/false positive rate and 75% quantile normalization as to eliminating technical variability and recovering true signal. Conclusions We present a statistical model based workflow for QC and normalizing GeoMx RNA data using GeoDiff, justified by statistical theory and validated by real/simulated data.
影响因子：
12.3
作者：
Hafemeister, Christoph;Satija, Rahul
通讯作者：
Satija, Rahul