Mariani Thomas J, Budhraja Vikram, Mecham Brigham H, Gu C Charles, Watson Mark A, Sadovsky Yoel
Division of Pulmonary and Critical Care, Department of Medicine, Brigham and Women's Hospital at Harvard Medical School, Boston, Massachusetts 02115, USA.
FASEB J. 2003 Feb;17(2):321-3. doi: 10.1096/fj.02-0351fje. Epub 2002 Dec 3.
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.
利用表达微阵列来确定不同实验范式之间基因表达的真实变化，会因测量变异性所产生的噪声而受到干扰。为了评估与转录本与基于商业寡核苷酸的微阵列杂交相关的变异性，我们使用Affymetrix人类U95基因芯片集，生成了一个数据集，该数据集包含来自三种不同实验范式的单个标记cRNA靶标的五次重复杂交。我们发现，我们数据集中表达水平的变异性是强度特异性的。我们对数据集中观察到的变异性进行了量化，以确定基因表达的显著变化。对重复样本标准差的绘图进行局部加权回归拟合，确定了与特定强度相关的变异性。这使得能够在任何统计置信水平下，计算出任何绝对强度的“可变倍数变化”阈值。对该方法的测试表明，它消除了强度特异性偏差，并使假阳性变化的数量减少了5至10倍。我们建议，即使在没有重复样本的情况下，这种方法也可广泛用于改进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。
