A variable fold-change threshold determines significance for expression microarrays
A variable fold-change threshold determines significance for expression microarrays
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DOI：
10.1096/fj.02-0351fje
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发表时间：
2002-12-01
期刊：
影响因子：
4.8
通讯作者：
Sadovsky, Y
中科院分区：
文献类型：
--
作者：
Mariani, TJ;Budhraja, V;Sadovsky, Y
由于测量的可变性，使用表达微阵列来确定实验范例之间的基因表达的真实变化受到噪声的干扰。为了评估与商业寡核苷酸微阵列转录本杂交相关的可变性，我们使用Affymetrix人U95基因芯片组生成了一个数据集，该数据集由来自三个不同实验范例的单个标记CRNA靶标的五个重复杂交组成。我们发现，我们数据集中表达水平的可变性是强度特定的。我们量化了我们数据集中观察到的可变性，以确定基因表达的显著变化。黄土拟合图的重复标准差赋予了与特定强度相关的变异性。这使得可以在任何统计置信度水平下计算任何绝对强度的“可变折叠变化”阈值。对该方法的测试表明，它消除了特定强度的偏差，并导致假阳性变化的数量减少了5-10倍。我们认为，这种方法可以广泛用于改善基于寡核苷酸的微阵列实验中基因表达显著变化的预测，并减少错误线索，即使在没有重复的情况下也是如此。
The use of expression microarrays to determine bona fide changes in gene expression between experimental paradigms is confounded by noise due to variability in measurement. To assess the variability associated with transcript hybridization to commercial oligonucleotide-based microarrays, we generated a data set consisting of five replicate hybridizations of a single labeled cRNA target from three distinct experimental paradigms, using the Affymetrix human U95 GeneChip set. We found that the variability of expression level in our data set is intensity-specific. We quantified the observed variability in our data set in order to determine significant changes in gene expression. LOESS fitting to a plot of the standard deviation of replicates assigned a variability associated with a specific intensity. This allowed for the calculation of a "variable fold-change" threshold for any absolute intensity at any level of statistical confidence. Testing of this method indicates that it removes intensity-specific bias and results in a 5- to 10-fold reduction in the number of false-positive changes. We suggest that this approach can be widely used to improve prediction of significant changes in gene expression for oligonucleotide-based microarray experiments and reduce false leads, even in the absence of replicates.