Establishing a major cause of discrepancy in the calibration of Affymetrix GeneChips.
Establishing a major cause of discrepancy in the calibration of Affymetrix GeneChips.
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DOI：
10.1186/1471-2105-8-195
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发表时间：
2007-06-11
影响因子：
3
通讯作者：
Orengo, Christine A
中科院分区：
文献类型：
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作者：
Harrison, Andrew P;Johnston, Caroline E;Orengo, Christine A
Affymetrix GeneChips 是一个流行的平台，用于在转录组上进行全基因组实验。有一系列不同的校准步骤，用户可以选择不同的背景扣除、归一化和表达测量。我们希望确定哪些校准步骤导致了报告差异表达的基因组中最大的不确定性。我们的结果表明，根据倍数变化的 z 分数估计，被确定为最显着差异表达的基因组对背景扣除和归一化的选择相对不敏感。然而，基因列表的内容对表达测量的选择最敏感。这与实验是否使用大鼠、小鼠或人类芯片以及芯片定义是否使用 Unigene、RefSeq、Entrez Gene 或原始 Affymetrix 定义的探针图谱进行无关。同样，无论是存在和不存在，还是仅存在，来自 MAS5 算法的调用都用于过滤基因列表，并且该结论对于不同强度的基因成立。在使用 t 统计分配基因进行差异表达后，我们也得出了相同的结论，尽管由于微阵列实验中通常使用的重复次数较少，这种方法会导致识别的基因组中出现大量假阳性。生物学家在分析 Affymetrix 数据时需要考虑的主要校准不确定性是如何将多个探针值压缩为一个表达测量值。
Affymetrix GeneChips are a popular platform for performing whole-genome experiments on the transcriptome. There are a range of different calibration steps, and users are presented with choices of different background subtractions, normalisations and expression measures. We wished to establish which of the calibration steps resulted in the biggest uncertainty in the sets of genes reported to be differentially expressed. Our results indicate that the sets of genes identified as being most significantly differentially expressed, as estimated by the z-score of fold change, is relatively insensitive to the choice of background subtraction and normalisation. However, the contents of the gene list are most sensitive to the choice of expression measure. This is irrespective of whether the experiment uses a rat, mouse or human chip and whether the chip definition is made using probe mappings from Unigene, RefSeq, Entrez Gene or the original Affymetrix definitions. It is also irrespective of whether both Present and Absent, or just Present, Calls from the MAS5 algorithm are used to filter genelists, and this conclusion holds for genes of differing intensities. We also reach the same conclusion after assigning genes to be differentially expressed using t-statistics, although this approach results in a large amount of false positives in the sets of genes identified due to the small numbers of replicates typically used in microarray experiments. The major calibration uncertainty that biologists need to consider when analysing Affymetrix data is how their multiple probe values are condensed into one expression measure.