Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays.
Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays.
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DOI：
10.1186/1471-2105-5-53
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发表时间：
2004-05-05
影响因子：
3
通讯作者：
Centola M
中科院分区：
文献类型：
--
作者：
Dozmorov I;Knowlton N;Tang Y;Centola M
微阵列数据分析的几个方面依赖于在检测极限或接近检测极限时表达的基因的鉴定。例如，基于回归的归一化方法依赖于一个前提，即比较样本中的大多数基因表达水平相似，因此需要准确识别非表达基因（加性噪声），以便将它们排除在归一化过程之外。此外，关键调控基因可以在低表达水平下保持对给定反应的严格控制。如果使用任意截断值来区分表达基因和非表达基因，一些关键的调控基因可能不必要地被排除在分析之外。不幸的是，目前还没有准确的方法来区分加性噪声和低水平表达的基因。我们开发了一种多步骤程序来分析mRNA表达数据，该程序可以在微阵列实验中可靠地识别加性噪声。这种分析是基于这样一个事实，即加性噪声信号可以通过分布和统计分析准确识别。以这种方式识别加性噪声可以从基于回归的比较曲线归一化中排除不相关的弱信号，从而最大限度地提高这些方法的准确性。此外，极低水平表达的基因由于其表达分布稳定，可与随机模式的加性噪声区分开来，因此可以清晰地识别。
Several aspects of microarray data analysis are dependent on identification of genes expressed at or near the limits of detection. For example, regression-based normalization methods rely on the premise that most genes in compared samples are expressed at similar levels and therefore require accurate identification of nonexpressed genes (additive noise) so that they can be excluded from the normalization procedure. Moreover, key regulatory genes can maintain stringent control of a given response at low expression levels. If arbitrary cutoffs are used for distinguishing expressed from nonexpressed genes, some of these key regulatory genes may be unnecessarily excluded from the analysis. Unfortunately, no accurate method for differentiating additive noise from genes expressed at low levels is currently available. We developed a multistep procedure for analysis of mRNA expression data that robustly identifies the additive noise in a microarray experiment. This analysis is predicated on the fact that additive noise signals can be accurately identified by both distribution and statistical analysis. Identification of additive noise in this manner allows exclusion of noncorrelated weak signals from regression-based normalization of compared profiles thus maximizing the accuracy of these methods. Moreover, genes expressed at very low levels can be clearly identified due to the fact that their expression distribution is stable and distinguishable from the random pattern of additive noise.
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影响因子：
5.8
作者：
Dozmorov, I;Centola, M
通讯作者：
Centola, M
影响因子：
4.6
作者：
Yang, MCK;Ruan, QG;She, JX
通讯作者：
She, JX
影响因子：
5.8
作者：
Rocke, DM;Durbin, B
通讯作者：
Durbin, B
影响因子：
4.6
作者：
Dozmorov, I;Saban, MR;Saban, R
通讯作者：
Saban, R
影响因子：
1.7
作者：
Rocke, DM;Durbin, B
通讯作者：
Durbin, B