Defining signal thresholds in DNA microarrays: exemplary application for invasive cancer.
Defining signal thresholds in DNA microarrays: exemplary application for invasive cancer.
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DNA微阵列中定义信号阈值：侵入性癌症的示例应用。
DOI：
10.1186/1471-2164-3-19
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发表时间：
2002-07-17
期刊：
影响因子：
4.4
通讯作者：
Quaranta, V
中科院分区：
文献类型：
--
作者：
Bilban, M;Buehler, LK;Head, S;Desoye, G;Quaranta, V
包含感兴趣基因子集的全基因组或应用靶向微阵列已被广泛用作具有诊断应用前景的研究工具。微阵列测量的内在变异性在定义缺失/存在或差异表达基因的信号阈值方面提出了一个主要问题。大多数策略使用倍数变化阈值，但低信号强度下的变异性可能使这种方法无效，并且它不能提供关于假阳性和假阴性的信息。介绍了一种从DNA微阵列实验中筛选假阳性和假阴性的方法。这是通过受试者工作特征（ROC）分析评价一组阳性和阴性对照来实现的。这种方法的一个优点是，用户可以根据敏感性和特异性考虑来定义阈值。ROC曲线下面积允许微阵列杂交的质量控制。这种方法已被应用于定制的微阵列开发的侵袭性黑色素瘤衍生的肿瘤细胞的分析。这表明，ROC分析产生了一个阈值，减少了微阵列实验中的错误分类基因。如果在微阵列上包括一组适当的阳性和阴性对照，ROC分析避免了在微阵列实验中任意选择阈值水平的固有问题。所提出的方法适用于定制和商业化的DNA微阵列，将有助于提高DNA微阵列实验预测的可靠性。
Genome-wide or application-targeted microarrays containing a subset of genes of interest have become widely used as a research tool with the prospect of diagnostic application. Intrinsic variability of microarray measurements poses a major problem in defining signal thresholds for absent/present or differentially expressed genes. Most strategies have used fold-change threshold values, but variability at low signal intensities may invalidate this approach and it does not provide information about false-positives and false negatives. We introduce a method to filter false-positives and false-negatives from DNA microarray experiments. This is achieved by evaluating a set of positive and negative controls by receiver operating characteristic (ROC) analysis. As an advantage of this approach, users may define thresholds on the basis of sensitivity and specificity considerations. The area under the ROC curve allows quality control of microarray hybridizations. This method has been applied to custom made microarrays developed for the analysis of invasive melanoma derived tumor cells. It demonstrated that ROC analysis yields a threshold with reduced missclassified genes in microarray experiments. Provided that a set of appropriate positive and negative controls is included on the microarray, ROC analysis obviates the inherent problem of arbitrarily selecting threshold levels in microarray experiments. The proposed method is applicable to both custom made and commercially available DNA microarrays and will help to improve the reliability of predictions from DNA microarray experiments.
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影响因子：
3.8
作者：
Bilban, M;Head, S;Quaranta, V
通讯作者：
Quaranta, V
影响因子：
2.9
作者：
Sakai, K;Higuchi, H;Kato, K
通讯作者：
Kato, K
影响因子：
4.6
作者：
Yang, MCK;Ruan, QG;She, JX
通讯作者：
She, JX
影响因子：
14.9
作者：
Mills, JC;Gordon, JI
通讯作者：
Gordon, JI
影响因子：
4.9
作者：
METZ, CE
通讯作者：
METZ, CE