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An expression index for Affymetrix GeneChips based on the generalized logarithm.

机译:基于广义对数的Affymetrix基因芯片的表达指数。

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MOTIVATION: Affymetrix GeneChip high-density oligonucleotide arrays interrogate a single transcript using multiple short 25mer probes. Usually, a necessary step in the analysis of experiments using these GeneChips is to summarize each of these probe sets into a single expression index that can then be used for determining differential expression, for classification, for clustering, and for other analyses. In this paper, we propose a new expression index that is competitive with the best existing methods, and superior in many cases. We call this expression index method GLA, for GLog Average, since after normalization at the probe level, we take the mean generalized logarithm of perfect match probes. RESULTS: In this paper, we use Affycomp as the primary tool to assess the weaknesses and strengths of GLA. Comparisons are made between GLA and most widely used summary methods (RMA, MAS5.0 and MBEI) in great detail. The substantial reduction in variability and increased ability to detect differential expression, together with the simplicity of implementation, make GLA a plausible candidate for analysis of Affymetrix GeneChip data.
机译:动机:Affymetrix GeneChip高密度寡核苷酸阵列使用多个短25mer探针探询单个转录物。通常,使用这些GeneChips分析实验的必要步骤是将每个探针集汇总为一个表达索引,然后将其用于确定差异表达,分类,聚类和其他分析。在本文中,我们提出了一种新的表达指数,该指数可与现有的最佳方法竞争,并且在许多情况下具有优越性。对于GLog平均,我们将这种表达索引方法称为GLA,因为在探针级别进行归一化之后,我们采用完美匹配探针的平均广义对数。结果:在本文中,我们使用Affycomp作为评估GLA弱点和优势的主要工具。 GLA与最广泛使用的汇总方法(RMA,MAS5.0和MBEI)进行了详细的比较。可变性的大大降低和检测差异表达的能力的增强以及实现的简便性,使得GLA成为分析Affymetrix GeneChip数据的合理候选者。

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