EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.
Genomics. 2010 Mar;95(3):138-42. doi: 10.1016/j.ygeno.2010.01.003. Epub 2010 Jan 14.
Microarrays have become a routine tool for biomedical research. Data quality assessment is an essential part of the analysis, but it is still not easy to perform objectively or in an automated manner, and as a result it is often neglected. Here, we compared two strategies of array-level quality control using five publicly available microarray experiments: outlier removal and array weights. We also compared them against no outlier removal and random array removal. We find that removing outlier arrays can improve the signal-to-noise ratio and thus strengthen the power of detecting differentially expressed genes. Using array weights is similarly effective, but its applicability is more limited. The quality metrics presented here are implemented in the Bioconductor package arrayQualityMetrics.
微阵列已成为生物医学研究的常规工具。数据质量评估是分析的一个重要组成部分，但它仍然不容易进行客观或自动化的评估，因此经常被忽视。在这里，我们比较了两种使用五个公开的微阵列实验的阵列级质量控制策略：异常值去除和阵列权重。我们还将它们与不进行异常值去除和随机阵列去除进行了比较。我们发现，去除异常值阵列可以提高信号噪声比，从而增强检测差异表达基因的能力。使用阵列权重同样有效，但适用性更有限。这里提出的质量指标已在 Bioconductor 包 arrayQualityMetrics 中实现。
