Boehringer Ingelheim Pharma GmbH & Co, KG, Birkendorfer Str, 65, 88397 Biberach/Riss, Germany.
BMC Genomics. 2010 Jun 2;11:349. doi: 10.1186/1471-2164-11-349.
Normalization of microarrays is a standard practice to account for and minimize effects which are not due to the controlled factors in an experiment. There is an overwhelming number of different methods that can be applied, none of which is ideally suited for all experimental designs. Thus, it is important to identify a normalization method appropriate for the experimental setup under consideration that is neither too negligent nor too stringent. Major aim is to derive optimal results from the underlying experiment. Comparisons of different normalization methods have already been conducted, none of which, to our knowledge, comparing more than a handful of methods.
In the present study, 25 different ways of pre-processing Illumina Sentrix BeadChip array data are compared. Among others, methods provided by the BeadStudio software are taken into account. Looking at different statistical measures, we point out the ideal versus the actual observations. Additionally, we compare qRT-PCR measurements of transcripts from different ranges of expression intensities to the respective normalized values of the microarray data. Taking together all different kinds of measures, the ideal method for our dataset is identified.
Pre-processing of microarray gene expression experiments has been shown to influence further downstream analysis to a great extent and thus has to be carefully chosen based on the design of the experiment. This study provides a recommendation for deciding which normalization method is best suited for a particular experimental setup.
微阵列的标准化是一种标准做法,用于说明和最小化不是由于实验中控制因素引起的效应。有大量不同的方法可以应用,没有一种方法是完全适合所有实验设计的。因此,识别适用于所考虑的实验设置的归一化方法非常重要,该方法既不过分疏忽也不过分严格。主要目的是从基础实验中得出最佳结果。已经对不同的归一化方法进行了比较,据我们所知,没有一种方法比较了超过几种方法。
在本研究中,比较了 25 种不同的预处理 Illumina Sentrix BeadChip 阵列数据的方法。其中包括 BeadStudio 软件提供的方法。通过观察不同的统计措施,我们指出了理想与实际观察之间的差异。此外,我们将不同表达强度范围的转录本的 qRT-PCR 测量值与微阵列数据的相应归一化值进行比较。综合所有不同的测量方法,确定了我们数据集的理想方法。
微阵列基因表达实验的预处理极大地影响了下游分析,因此必须根据实验设计仔细选择。本研究为决定哪种归一化方法最适合特定的实验设置提供了建议。