Binder Hans, Preibisch Stephan, Berger Hilmar
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
Methods Mol Biol. 2010;576:375-407. doi: 10.1007/978-1-59745-545-9_20.
Calibration of microarray measurements aims at removing systematic biases from the probe-level data to get expression estimates that linearly correlate with the transcript abundance in the studied samples. The improvement of calibration methods is an essential prerequisite for estimating absolute expression levels, which, in turn, are required for quantitative analyses of transcriptional regulation, for example, in the context of gene profiling of diseases. We address hybridization on microarrays as a reaction process in a complex environment and express the measured intensities as a function of the input quantities of the experiment. Popular calibration methods such as MAS5, dChip, RMA, gcRMA, vsn, and PLIER are briefly reviewed and assessed in light of the hybridization model and of previous benchmark studies. We present our hook method, a new calibration approach that is based on a graphical summary of the actual hybridization characteristics of a particular microarray. Although single-chip related, hook performs as well as the multi-chip-related gcRMA, presently one of the best state-of-the-art methods for estimating expression values. The hook method, in addition, provides a set of chip summary characteristics that evaluate the performance of a given hybridization. The algorithm of the method is briefly described and its performance is exemplified.
微阵列测量的校准旨在从探针水平数据中去除系统偏差，以获得与所研究样本中转录本丰度呈线性相关的表达估计值。校准方法的改进是估计绝对表达水平的必要前提条件，而绝对表达水平又是转录调控定量分析所必需的，例如在疾病基因谱分析的背景下。我们将微阵列上的杂交视为复杂环境中的一个反应过程，并将测量强度表示为实验输入量的函数。根据杂交模型和先前的基准研究，对诸如MAS5、dChip、RMA、gcRMA、vsn和PLIER等常用校准方法进行了简要回顾和评估。我们提出了钩法，这是一种基于特定微阵列实际杂交特征的图形化总结的新校准方法。尽管与单芯片相关，但钩法的性能与多芯片相关的gcRMA相当，而gcRMA是目前估计表达值的最佳先进方法之一。此外，钩法还提供了一组芯片总结特征，用于评估给定杂交的性能。简要描述了该方法的算法，并举例说明了其性能。
