A new approach for filtering noise from high-density oligonucleotide microarray datasets
A new approach for filtering noise from high-density oligonucleotide microarray datasets
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DOI：
10.1093/nar/29.15.e72
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发表时间：
2001-08-01
影响因子：
14.9
通讯作者：
Gordon, JI
中科院分区：
文献类型：
--
作者：
Mills, JC;Gordon, JI
尽管DNA微阵列是分析基因表达的有力工具，但是产生的信号的动态范围和绝对数量需要有效的程序来区分假阳性结果（噪声）与“真实的”（独立可再现的）表达变化。我们已经开发了一种方法来过滤噪音时产生的数据集高密度的基于谷胱甘肽的微阵列被用来比较两个不同的RNA群体。首先，我们在与从相同的起始RNA群体制备的cRNA杂交的芯片之间进行比较;在这种比较中的“增加”或“减少”调用被定义为假阳性。绘制这些假阳性信号强度在9个独立RNA制备物的18个这样的比较中的平均分布，使我们能够开发一系列噪声过滤查找表（LUT）。使用由不同工作人员在不同地点和不同时间制备的不同RNA制剂之间的70个单独芯片到芯片比较的数据库，我们表明LUT可用于预测在一次比较中称为增加或减少的给定转录物在重复比较中再次被称为增加或减少的可能性。有证据表明，这种基于LUT的评分系统提供了更大的预测价值，可重复的微阵列结果比强加任意倍数变化阈值，并准确地预测微阵列识别的变化将被验证的独立检测，如定量实时PCR。
Although DNA microarrays are powerful tools for profiling gene expression, the dynamic range and the sheer number of signals produced require efficient procedures for distinguishing false positive results (noise) from changes in expression that are 'real' (independently reproducible). We have developed an approach to filter noise from datasets generated when high density oligonucleotide-based microarrays are used to compare two distinct RNA populations. First, we performed comparisons between chips hybridized with cRNAs prepared from an identical starting RNA population; an 'Increase' or 'Decrease' call in such a comparison was defined as a false positive. Plotting the average distribution of these false positive signal intensities across 18 such comparisons of nine independent RNA preparations allowed us to develop a series of noise-filtering lookup tables (LUTs). Using a database of 70 separate chip-to-chip comparisons between distinct RNA preparations prepared by different workers at different sites and at different times, we show that the LUTs can be used to predict the likelihood that a given transcript called Increased or Decreased in one comparison will again be called Increased or Decreased in a replicate comparison. Evidence is presented that this LUT-based scoring system provides greater predictive value for reproducible microarray results than imposition of arbitrary fold-change thresholds and accurately predicts which microarray-identified changes will be validated by Independent assays such as quantitative real-time PCR.