Detection call algorithms for high-throughput gene expression microarray data
Detection call algorithms for high-throughput gene expression microarray data
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DOI：
10.1093/bib/bbp055
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发表时间：
2010-03-01
影响因子：
9.5
通讯作者：
Reese, Sarah E.
中科院分区：
文献类型：
--
作者：
Archer, Kellie J.;Reese, Sarah E.
已经进行了广泛的方法学研究以改进基因表达汇总方法。然而，除了定量基因表达总结，大多数平台，包括所有在微阵列质量控制项目中检查的平台，提供了平台上每个基因的定性检测调用结果。这些检测调用算法旨在评估每个转录本是否被可靠地测量。在本文中，我们回顾使用这些定性检测调用结果的微阵列数据的分析。我们还审查了两个广泛使用的基因表达微阵列平台，Affychip GeneChips和Illumina BeadArrays的检测调用算法，并更清楚地形式化Illumina BeadArray检测调用算法的数学符号。两种算法都产生P值，然后将其用于确定定性检测调用。我们通过将这些方法应用于两个尖峰数据集来检查这些检测调用算法和默认参数的性能。我们表明，定性检测调用的默认参数产生很少缺席的呼吁高加标浓度。当预期目标基因以非常低的浓度存在时，加标数据集可用于适当调整定性检测调用的调谐参数。
Extensive methodological research has been conducted to improve gene expression summary methods. However, in addition to quantitative gene expression summaries, most platforms, including all those examined in the MicroArray Quality Control project, provide a qualitative detection call result for each gene on the platform. These detection call algorithms are intended to render an assessment of whether or not each transcript is reliably measured. In this paper, we review uses of these qualitative detection call results in the analysis of microarray data. We also review the detection call algorithms for two widely used gene expression microarray platforms, Affymetrix GeneChips and Illumina BeadArrays, and more clearly formalize the mathematical notation for the Illumina BeadArray detection call algorithm. Both algorithms result in a P-value which is then used for determining the qualitative detection calls. We examined the performance of these detection call algorithms and default parameters by applying the methods to two spike-in datasets. We show that the default parameters for qualitative detection calls yield few absent calls for high spike-in concentrations. When genes of interest are expected to be present at very low concentrations, spike-in datasets can be useful for appropriately adjusting the tuning parameters for qualitative detection calls.