Cope Leslie M, Irizarry Rafael A, Jaffee Harris A, Wu Zhijin, Speed Terence P
Department of Mathematical Sciences, Johns Hopkins University, 104 Whitehead Hall, 3400 North Charles Street, Baltimore, MD 21218, USA.
Bioinformatics. 2004 Feb 12;20(3):323-31. doi: 10.1093/bioinformatics/btg410.
The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry.
We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data.
寡核苷酸表达阵列的显著特征是使用多个探针来检测每个目标转录本。这对于统计遗传学家来说是一个福音，他们可以创建具有特定特征的探针集汇总。目前有几种方法可用于汇总来自流行的Affymetrix基因芯片的探针水平数据，但很难为给定的研究确定最佳方法。
我们开发了一种图形工具来评估Affymetrix探针水平数据的汇总。图表和汇总统计提供了一个关于表达量度在几个重要方面表现的图景。这一图景有助于比较相互竞争的表达量度，并选择适合特定研究的方法。关键是一个由稀释研究和掺入研究组成的基准数据集。由于这些数据的真实情况是已知的，我们可以识别数据的统计特征，而这些特征的预期结果是预先已知的。我们这套图表中突出显示的那些特征是由生物学兴趣问题证明合理的，并且是由适当数据的存在所推动的。
