Binder Hans, Krohn Knut, Preibisch Stephan
Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany.
Algorithms Mol Biol. 2008 Aug 29;3:11. doi: 10.1186/1748-7188-3-11.
Microarray experiments rely on several critical steps that may introduce biases and uncertainty in downstream analyses. These steps include mRNA sample extraction, amplification and labelling, hybridization, and scanning causing chip-specific systematic variations on the raw intensity level. Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures. In the accompanying publication we presented theory and algorithm of the so-called hook method which aims at correcting expression data for systematic biases using a series of new chip characteristics.
In this publication we summarize the essential chip characteristics provided by this method, analyze special benchmark experiments to estimate transcript related expression measures and illustrate the potency of the method to detect and to quantify the quality of a particular hybridization. It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude. In addition, the method calculates a detection call judging the relation between the signal and the detection limit of the particular measurement. The performance of the method in the context of different chip generations and probe set assignments is illustrated. The hook method characterizes the RNA-quality in terms of the 3'/5'-amplification bias and the sample-specific calling rate. We show that the proper judgement of these effects requires the disentanglement of non-specific and specific hybridization which, otherwise, can lead to misinterpretations of expression changes. The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.
The single-chip based hook-method provides accurate expression estimates and chip-summary characteristics using the natural metrics given by the hybridization reaction with the potency to develop new standards for microarray quality control and calibration.
微阵列实验依赖于几个关键步骤，这些步骤可能会在下游分析中引入偏差和不确定性。这些步骤包括mRNA样本提取、扩增和标记、杂交以及扫描，从而在原始强度水平上产生芯片特异性的系统变化。此外，所选的阵列类型以及芯片上探测的基因组信息的时效性也会影响表达测量的质量。在随附的出版物中，我们提出了所谓的钩子法的理论和算法，该方法旨在利用一系列新的芯片特征校正表达数据中的系统偏差。
在本出版物中，我们总结了该方法提供的基本芯片特征，分析了特殊的基准实验以估计转录本相关的表达测量，并说明了该方法检测和量化特定杂交质量的能力。结果表明，我们的单芯片方法提供的表达测量在转录本浓度变化三个数量级上呈线性响应。此外，该方法计算一个检测判定，以判断信号与特定测量的检测限之间的关系。阐述了该方法在不同芯片代次和探针集分配情况下的性能。钩子法根据3'/5'-扩增偏差和样本特异性检出率来表征RNA质量。我们表明，对这些效应的正确判断需要区分非特异性和特异性杂交，否则可能导致对表达变化的误解。展示了通过改变标记方案或用DNA靶标替代RNA来改变探针/靶标相互作用的后果。
基于单芯片的钩子法利用杂交反应给出的自然指标提供准确的表达估计和芯片总结特征，有潜力为微阵列质量控制和校准制定新的标准。
