A benchmark for affymetrix GeneChip expression measures
A benchmark for affymetrix GeneChip expression measures
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DOI：
10.1093/bioinformatics/btg410
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发表时间：
2004-02-12
期刊：
影响因子：
5.8
通讯作者：
Speed, TP
中科院分区：
文献类型：
--
作者：
Cope, LM;Irizarry, RA;Speed, TP
动机：寡核苷酸表达阵列的定义特征是使用几个探针来分析每个靶向转录本。这对统计遗传学家来说是一笔巨大的财富，他们可以创建具有特定特征的概率集摘要。目前流行的Affymetrix GeneChips有多种方法可用于汇总探针级数据，但很难确定最适合特定查询的方法。结果：我们开发了一个图形化工具来评估Affymetrix探针级数据汇总。图表和汇总统计数据提供了表达式测量在几个重要领域的表现情况。这张图便于比较相互竞争的表达方法，并选择适合特定调查的方法。关键是由稀释研究和尖峰研究组成的基准数据集。因为这些数据的真相是已知的，所以我们可以识别数据的统计特征，从而提前知道其预期结果。我们的图表中突出的那些特征是由生物学上感兴趣的问题证明的，并由适当数据的存在所激励。
Motivation: 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.Results: 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.