Department of Pharmacology and Toxicology, University of Utah, 201 Presidents Circle, Salt Lake City, UT 84112, USA.
Genomics. 2012 Dec;100(6):337-44. doi: 10.1016/j.ygeno.2012.08.003. Epub 2012 Aug 19.
Gene-expression microarrays allow researchers to characterize biological phenomena in a high-throughput fashion but are subject to technological biases and inevitable variabilities that arise during sample collection and processing. Normalization techniques aim to correct such biases. Most existing methods require multiple samples to be processed in aggregate; consequently, each sample's output is influenced by other samples processed jointly. However, in personalized-medicine workflows, samples may arrive serially, so renormalizing all samples upon each new arrival would be impractical. We have developed Single Channel Array Normalization (SCAN), a single-sample technique that models the effects of probe-nucleotide composition on fluorescence intensity and corrects for such effects, dramatically increasing the signal-to-noise ratio within individual samples while decreasing variation across samples. In various benchmark comparisons, we show that SCAN performs as well as or better than competing methods yet has no dependence on external reference samples and can be applied to any single-channel microarray platform.
基因表达微阵列使研究人员能够以高通量的方式描述生物现象，但它们受到技术偏差和样本收集和处理过程中不可避免的变异的影响。归一化技术旨在纠正这些偏差。大多数现有方法需要将多个样本集中处理；因此，每个样本的输出都会受到其他共同处理的样本的影响。然而，在个性化医疗工作流程中，样本可能会陆续到达，因此对每个新到达的样本重新进行归一化是不切实际的。我们开发了单通道阵列归一化（SCAN），这是一种单样本技术，它对探针核苷酸组成对荧光强度的影响进行建模，并对其进行校正，从而显著提高单个样本内的信号噪声比，同时降低样本之间的变异。在各种基准比较中，我们表明 SCAN 的性能与竞争方法一样好或更好，但不依赖于外部参考样本，并且可以应用于任何单通道微阵列平台。
