Simulated annealing of microarray data reduces noise and enables cross-experimental comparisons.
Simulated annealing of microarray data reduces noise and enables cross-experimental comparisons.
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DOI：
10.1089/dna.2004.23.695
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发表时间：
2004-10
影响因子：
3.1
通讯作者：
J. Wren;Minghua Yao;M. Langer;T. Conway
中科院分区：
文献类型：
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作者：
J. Wren;Minghua Yao;M. Langer;T. Conway
微阵列是评估对内部或外部刺激的转录反应的全基因组诱导的强大工具，但不被认为是定量严格的（即，杂交探针的信号强度通常用于量化相对转录物丰度）。因此，在没有参考标准的情况下，即使不是不可能，也很难准确地比较单独的微阵列实验。然而，即使在重复的微阵列实验中，每个基因在检测到的信号量上也存在显着差异，这表明没有一个基因适合作为标准。我们提出并测试了一种使用模拟退火（SA）将实验转录谱与一组参考实验“对齐”的方法，本质上使用所有基因的相对位置作为参考标准。 SA 试图在给定先前观察到的基因表达测量范围的情况下，为每个实验基因表达信号的相对表达水平找到全局最佳调整因子。通过在控制条件下定义所有基因的基因表达的相对动态范围，我们可以更准确地比较单独实验之间以及可能物种之间的转录谱，从而实现比较转录组学。在已发布的数据集上测试 SA，我们发现它显着减少了实验间的变异，这表明它有望实现这一目标。
Microarrays are a powerful tool for assessing the genome-wide induction of a transcriptional response to internal or external stimuli, but are not considered quantitatively rigorous (i.e., the signal intensity of hybridized probe is normally used to quantify relative transcript abundance). Thus, it is difficult, if not impossible, to accurately compare separate microarray experiments without a reference standard. However, even among replicated microarray experiments, each gene varies significantly in the amount of signal detected, suggesting no single gene would be appropriate as a standard. We propose and test a method to "align" experimental transcription profiles to a set of reference experiments using simulated annealing (SA), essentially using the relative positions of all genes as a reference standard. SA attempts to find a globally optimal adjustment factor for the relative expression level of each experimental gene expression signal, given a previously observed range of gene expression measurements. By defining a relative dynamic range of gene expression under control conditions for all genes, we can more accurately compare transcription profiles between separate experiments and, potentially, between species--enabling comparative transcriptomics. Testing SA on a published dataset, we find that it significantly reduces interexperimental variation, suggesting it holds promise to accomplish this goal.