Karakach Tobias K, Flight Robert M, Wentzell Peter D
Department of Chemistry, Dalhousie University, Halifax, Nova Scotia, B3H 4J3, Canada.
Anal Bioanal Chem. 2007 Dec;389(7-8):2125-41. doi: 10.1007/s00216-007-1617-0. Epub 2007 Sep 27.
DNA microarrays permit the measurement of gene expression across the entire genome of an organism, but the quality of the thousands of measurements is highly variable. For spotted dual-color microarrays the situation is complicated by the use of ratio measurements. Studies have shown that measurement errors can be described by multiplicative and additive terms, with the latter dominating for low-intensity measurements. In this work, a measurement-error model is presented that partitions the variance into general experimental sources and sources associated with the calculation of the ratio from noisy pixel data. The former is described by a proportional (multiplicative) structure, while the latter is estimated using a statistical bootstrap method. The model is validated using simulations and three experimental data sets. Monte-Carlo fits of the model to data from duplicate experiments are excellent, but suggest that the bootstrap estimates, while proportionately correct, may be underestimated. The bootstrap standard error estimates are particularly useful in determining the reliability of individual microarray spots without the need for replicate spotting. This information can be used in screening or weighting the measurements.
DNA微阵列技术能够对生物体的整个基因组的基因表达进行测量，但是数千次测量的质量存在很大差异。对于点阵双色微阵列，由于采用比值测量，情况变得更加复杂。研究表明，测量误差可以用乘法项和加法项来描述，在低强度测量中，加法项占主导地位。在这项工作中，我们提出了一个测量误差模型，该模型将方差划分为一般实验来源和与从噪声像素数据计算比值相关的来源。前者由比例（乘法）结构描述，而后者使用统计自举法进行估计。该模型通过模拟和三个实验数据集进行了验证。该模型对重复实验数据的蒙特卡罗拟合效果极佳，但表明自举估计虽然在比例上是正确的，但可能被低估。自举标准误差估计在确定单个微阵列点的可靠性方面特别有用，而无需重复点样。此信息可用于筛选或权衡测量结果。
