Consolidated strategy for the analysis of microarray spike-in data
Consolidated strategy for the analysis of microarray spike-in data
复制标题
DOI：
10.1093/nar/gkn430
复制
发表时间：
2008-10-01
影响因子：
14.9
通讯作者：
Irizarry, Rafael A.
中科院分区：
文献类型：
--
作者：
McCall, Matthew N.;Irizarry, Rafael A.
随着微阵列技术用户数量的持续增长，平台评估和比较的重要性也在增加。加标实验已成功地用于微阵列制造商的内部技术评估和竞争数据分析方法的比较。微阵列文献充满了基于加标实验数据的统计评估。遗憾的是，统计评估差异很大，仅适用于特定情况。这给关于最佳实践的辩论带来了混乱，即哪些平台、协议和数据分析工具是最好的。此外，跨平台比较已被证明是困难的，因为报告的浓度不可比。在这篇文章中，我们介绍了两个新的spike-in实验，提出了一种新的统计解决方案，使跨平台的比较，并提出了一个全面的评估程序的基础上spike-in实验。这些想法是在一个用户友好的Bioconductor包中实现的：spkTools。我们展示了我们的工具的实用性，提出了第一个尖峰在三个主要平台的比较-Affytounts，安捷伦和Illumina。
As the number of users of microarray technology continues to grow, so does the importance of platform assessments and comparisons. Spike-in experiments have been successfully used for internal technology assessments by microarray manufacturers and for comparisons of competing data analysis approaches. The microarray literature is saturated with statistical assessments based on spike-in experiment data. Unfortunately, the statistical assessments vary widely and are applicable only in specific cases. This has introduced confusion into the debate over best practices with regards to which platform, protocols and data analysis tools are best. Furthermore, cross-platform comparisons have proven difficult because reported concentrations are not comparable. In this article, we introduce two new spike-in experiments, present a novel statistical solution that enables cross-platform comparisons, and propose a comprehensive procedure for assessments based on spike-in experiments. The ideas are implemented in a user friendly Bioconductor package: spkTools. We demonstrated the utility of our tools by presenting the first spike-in-based comparison of the three major platforms-Affymetrix, Agilent and Illumina.