MDQC: a new quality assessment method for microarrays based on quality control reports
MDQC: a new quality assessment method for microarrays based on quality control reports
复制标题
DOI：
10.1093/bioinformatics/btm487
复制
发表时间：
2007-12-01
期刊：
影响因子：
5.8
通讯作者：
Ng, Raymond T.
中科院分区：
文献类型：
--
作者：
Freue, Gabriela V. Cohen;Hollander, Zsuzsanna;Ng, Raymond T.
动机：产生微阵列数据的过程涉及多个步骤，其中一些步骤可能会遇到技术问题并严重损害数据的质量。因此，识别低质量的阵列是必要的。这篇文章解决了两个问题：（1）如何评估质量的微阵列数据集使用的质量控制（QC）报告中提供的措施;（2）如何识别可能的来源的质量problems.Results：我们提出了一种新的多元方法来评估阵列的质量，检查其质量属性的马氏距离从其他阵列。因此，我们称之为马氏距离质量控制（MDQC），并检查这种方法的不同方法。MDQC基于离群值检测的思想标记有问题的数组，即标记那些质量属性共同偏离大部分数据的数组。使用两个案例研究，我们表明，多变量分析提供了更丰富的信息，比孤立地分析每个参数的QC报告。此外，一旦生成QC报告，我们的质量评估方法在计算上是廉价的，结果可以很容易地可视化和解释。最后，我们表明，在报告中的质量措施的子集上计算这些距离可能会增加检测异常阵列的方法的能力，并有助于确定质量问题的可能原因。
Motivation: The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems.Results: We propose a novel multivariate approach to evaluate the quality of an array that examines the Mahalanobis distance of its quality attributes from those of other arrays. Thus, we call it Mahalanobis Distance Quality Control (MDQC) and examine different approaches of this method. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Using two case studies, we show that a multivariate analysis gives substantially richer information than analyzing each parameter of the QC report in isolation. Moreover, once the QC report is produced, our quality assessment method is computationally inexpensive and the results can be easily visualized and interpreted. Finally, we show that computing these distances on subsets of the quality measures in the report may increase the methods ability to detect unusual arrays and helps to identify possible reasons of the quality problems.