The efficiency of pooling mRNA in microarray experiments
The efficiency of pooling mRNA in microarray experiments
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DOI：
10.1093/biostatistics/4.3.465
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发表时间：
2003-07-01
期刊：
影响因子：
2.1
通讯作者：
Attie, AD
中科院分区：
文献类型：
--
作者：
Kendziorski, CM;Zhang, Y;Attie, AD
在微阵列实验中，允许在受试者之间汇集了信使RNA样品，或者是出于必要性或努力减少生物学变异的效果。这种实验的一个基本问题是估计大量基因的标称表达水平。合并样品会影响表达估计，但确切的效果尚未被称为该方法在这种情况下尚未系统地研究。我们考虑通过评估不同估计量的有限样本性能在具有和没有合并的设计的设计中，如何通过评估不同估计量的有限样本性能来影响表达估计。定义了池mRNA有利的条件；在这些条件下得出了汇总和非流动设计的估计值的一般特性。给出了一个合并实验中所需的受试者和阵列总数的公式，以获得与从无功能情况获得的基因表达估计值和置信区间相当的公式。该公式表明，通过汇总可能增加的受试者数量，可以减少实验中所需的阵列数量而不会损失精度。使用来自定量实时PCR实验的数据，考虑了促进该公式的促进的假设。该计算不是一种特定的量化基因表达的方法，因为它们仅假定为每个基因获得一个单一的，归一化的表达估计。因此，结果通常应适用于许多技术，只要提供足够的预处理和标准化方法，就可以使用并应用。
In a microarray experiment, messenger RNA samples are oftentimes pooled across subjects out of necessity, or in an effort to reduce the effect of biological variation. A basic problem in such experiments is to estimate the nominal expression levels of a large number of genes. Pooling samples will affect expression estimation, but the exact effects are not yet known as the approach has not been systematically studied in this context. We consider how mRNA pooling affects expression estimates by assessing the finite-sample performance of different estimators for designs with and without pooling. Conditions under which it is advantageous to pool mRNA are defined; and general properties of estimates from both pooled and non-pooled designs are derived under these conditions. A formula is given for the total number of subjects and arrays required in a pooled experiment to obtain gene expression estimates and confidence intervals comparable to those obtained from the no-pooling case. The formula demonstrates that by pooling a perhaps increased number of subjects, one can decrease the number of arrays required in an experiment without a loss of precision. The assumptions that facilitate derivation of this formula are considered using data from a quantitative real-time PCR experiment. The calculations are not specific to one particular method of quantifying gene expression as they assume only that a single, normalized, estimate of expression is obtained for each gene. As such, the results should be generally applicable to a number of technologies provided sufficient pre-processing and normalization methods are available and applied.