Department of Biostatistics, Johns Hopkins University Bloomberg, School of Public Health, Baltimore, MD, USA.
Department of Epidemiology, Johns Hopkins University Bloomberg, School of Public Health, Baltimore, MD, USA.
Proteomics. 2022 Feb;22(3):e2100033. doi: 10.1002/pmic.202100033. Epub 2021 Oct 27.
Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.
技术变异，或非生物来源的变异，存在于大多数实验室检测中。纠正这种变异可以使分析人员提取出一个生物学信号，从而回答感兴趣的问题。然而，每种检测都有不同的技术变异来源和水平，并且校正方法的选择会影响下游分析。与 DNA 微阵列等类似的检测相比，用于蛋白质微阵列的方法相对较少，蛋白质微阵列是一种用于测量血清样本中各种蛋白质水平的多功能工具。在这里，我们提出了一个预处理管道，以纠正蛋白质微阵列中一些常见的技术变异来源。该管道通过使用对照来减少技术变异，构建在现有的归一化方法之上。我们使用来自两个蛋白质微阵列研究的数据和模拟来评估我们的方法。我们证明预处理选择会影响荧光强度的蛋白排序，而这反过来又会影响下游分析。
