Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis.
Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis.
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RNA滴定系列的统计分析在整个阵列中评估了微阵列的精度和灵敏度。
DOI：
10.1186/1471-2105-7-511
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发表时间：
2006-11-22
影响因子：
3
通讯作者：
Smyth, Gordon K.
中科院分区：
文献类型：
--
作者：
Holloway, Andrew J.;Oshlack, Alicia;Diyagama, Dileepa S.;Bowtell, David D. L.;Smyth, Gordon K.
人们经常担心微阵列技术的准确性和跨平台一致性程度，但目前还没有方法可以在整个阵列的基础上明确评估这些技术的精度和灵敏度。描述了一种用于评估全基因组基因表达技术（例如微阵列）的精度和灵敏度的方法。该方法由易于构建的 RNA 样品滴定系列和使用非线性回归的相关统计分析组成。该方法在整个阵列的基础上评估每个微阵列平台的精度和响应性，即使用所有探针，而不需要跨平台匹配探针。进行了一项实验来评估和比较四种广泛使用的微阵列平台。所有四个平台均显示出令人满意的精度，但商业平台更适合解决较低表达水平基因的差异表达。通过在统计模型中考虑探针特定的染料效应，可以提高双色平台的有效精度。该方法用于比较 Affymetrix 平台的三种数据提取算法，证明常用专有算法相对于其他算法的性能较差。对于可以跨平台匹配的探针，跨平台变异性被分解为平台内和平台间组件，表明平台差异几乎完全是系统性的，而不是由于测量变异性。结果表明所有平台都具有良好的精度和灵敏度，但强调需要改进探针注释。它们量化了在预测疾病进展或结果方面，跨平台测量的准确度预计低于平台内比较。
Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis. A methodology is described for evaluating the precision and sensitivity of whole-genome gene expression technologies such as microarrays. The method consists of an easy-to-construct titration series of RNA samples and an associated statistical analysis using non-linear regression. The method evaluates the precision and responsiveness of each microarray platform on a whole-array basis, i.e., using all the probes, without the need to match probes across platforms. An experiment is conducted to assess and compare four widely used microarray platforms. All four platforms are shown to have satisfactory precision but the commercial platforms are superior for resolving differential expression for genes at lower expression levels. The effective precision of the two-color platforms is improved by allowing for probe-specific dye-effects in the statistical model. The methodology is used to compare three data extraction algorithms for the Affymetrix platforms, demonstrating poor performance for the commonly used proprietary algorithm relative to the other algorithms. For probes which can be matched across platforms, the cross-platform variability is decomposed into within-platform and between-platform components, showing that platform disagreement is almost entirely systematic rather than due to measurement variability. The results demonstrate good precision and sensitivity for all the platforms, but highlight the need for improved probe annotation. They quantify the extent to which cross-platform measures can be expected to be less accurate than within-platform comparisons for predicting disease progression or outcome.
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