López-Romero Pedro, González Manuel A, Callejas Sergio, Dopazo Ana, Irizarry Rafael A
Department of Cardiovascular Epidemiology and Population Genetics, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.
BMC Res Notes. 2010 Jan 22;3:18. doi: 10.1186/1756-0500-3-18.
The Agilent microRNA microarray platform interrogates each microRNA with several copies of distinct oligonucleotide probes and integrates the results into a total gene signal (TGS), using a proprietary algorithm that makes use of the background subtracted signal. The TGS can be normalized between arrays, and the Agilent recommendation is either not to normalize or to normalize to the 75th percentile signal intensity. The robust multiarray average algorithm (RMA) is an alternative method, originally developed to obtain a summary measure of mRNA Affymetrix gene expression arrays by using a linear model that takes into account the probe affinity effect. The RMA method has been shown to improve the accuracy and precision of expression measurements relative to other competing methods. There is also evidence that it might be preferable to use non-corrected signals for the processing of microRNA data, rather than background-corrected signals. In this study we assess the use of the RMA method to obtain a summarized microRNA signal for the Agilent arrays.
We have adapted the RMA method to obtain a processed signal for the Agilent arrays and have compared the RMA summarized signal to the TGS generated with the image analysis software provided by the vendor. We also compared the use of the RMA algorithm with uncorrected and background-corrected signals, and compared quantile normalization with the normalization method recommended by the vendor. The pre-processing methods were compared in terms of their ability to reduce the variability (increase precision) of the signals between biological replicates. Application of the RMA method to non-background corrected signals produced more precise signals than either the RMA-background-corrected signal or the quantile-normalized Agilent TGS. The Agilent TGS normalized to the 75% percentile showed more variation than the other measures.
Used without background correction, a summarized signal that takes into account the probe effect might provide a more precise estimate of microRNA expression. The variability of quantile normalization was lower compared with the normalization method recommended by the vendor.
安捷伦微RNA微阵列平台使用多个不同的寡核苷酸探针拷贝来检测每个微RNA，并利用一种利用背景扣除信号的专有算法将结果整合为总基因信号（TGS）。TGS可在各阵列之间进行标准化，安捷伦的建议是要么不进行标准化，要么标准化到第75百分位数信号强度。稳健多阵列平均算法（RMA）是一种替代方法，最初是为通过使用考虑探针亲和力效应的线性模型来获得mRNA Affymetrix基因表达阵列的汇总测量值而开发的。相对于其他竞争方法，RMA方法已被证明可提高表达测量的准确性和精度。也有证据表明，在处理微RNA数据时，使用未校正信号可能比背景校正信号更可取。在本研究中，我们评估了使用RMA方法为安捷伦阵列获得汇总微RNA信号的情况。
我们对RMA方法进行了调整，以获得安捷伦阵列的处理后信号，并将RMA汇总信号与使用供应商提供的图像分析软件生成的TGS进行了比较。我们还比较了使用未校正和背景校正信号的RMA算法，并将分位数标准化与供应商推荐的标准化方法进行了比较。根据减少生物重复之间信号变异性（提高精度）的能力对预处理方法进行了比较。将RMA方法应用于未背景校正的信号产生的信号比RMA背景校正信号或分位数标准化的安捷伦TGS更精确。标准化到第75百分位数的安捷伦TGS显示出比其他测量更多的变异性。
在不进行背景校正的情况下使用时，考虑探针效应的汇总信号可能会提供对微RNA表达更精确估计。与供应商推荐的标准化方法相比，分位数标准化的变异性较低。
