Evaluating strategies to normalise biological replicates of Western blot data.
Evaluating strategies to normalise biological replicates of Western blot data.
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DOI：
10.1371/journal.pone.0087293
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Kholodenko BN
中科院分区：
文献类型：
--
作者：
Degasperi A;Birtwistle MR;Volinsky N;Rauch J;Kolch W;Kholodenko BN
蛋白质印迹数据广泛用于定量应用，如统计测试和数学建模。为了确保准确的定量和实验之间的可比性，Western印迹重复必须归一化，但目前还不清楚可用的方法如何影响数据的统计特性。在这里，我们评估了三种常用的标准化策略：（i）通过固定的标准化点或对照;（ii）通过重复中所有数据点的总和;以及（iii）通过重复的最佳对齐。我们考虑这些不同的策略如何影响变异系数（CV）和标准化数据的假设检验结果。通过固定点进行归一化往往会增加归一化数据的平均CV，其方式自然取决于归一化点的选择。因此，在假设检验的上下文中，通过固定点的标准化减少了假阳性并增加了假阴性。对已发表的实验数据的分析表明，选择具有低定量强度的归一化点会导致高归一化数据CV，因此应避免。通过求和或通过最佳对齐进行的归一化以平均值依赖的方式重新分布原始数据的不确定性，降低高强度点的CV并增加低强度点的CV。这导致通过求和或最佳比对对假设检验的归一化的影响取决于测试数据的平均值;对于高强度点，假阳性增加，假阴性减少，而对于低强度点，假阳性减少，假阴性增加。这些结果将帮助Western印迹的用户选择合适的标准化策略，并了解这种标准化对后续假设检验的影响。
Western blot data are widely used in quantitative applications such as statistical testing and mathematical modelling. To ensure accurate quantitation and comparability between experiments, Western blot replicates must be normalised, but it is unclear how the available methods affect statistical properties of the data. Here we evaluate three commonly used normalisation strategies: (i) by fixed normalisation point or control; (ii) by sum of all data points in a replicate; and (iii) by optimal alignment of the replicates. We consider how these different strategies affect the coefficient of variation (CV) and the results of hypothesis testing with the normalised data. Normalisation by fixed point tends to increase the mean CV of normalised data in a manner that naturally depends on the choice of the normalisation point. Thus, in the context of hypothesis testing, normalisation by fixed point reduces false positives and increases false negatives. Analysis of published experimental data shows that choosing normalisation points with low quantified intensities results in a high normalised data CV and should thus be avoided. Normalisation by sum or by optimal alignment redistributes the raw data uncertainty in a mean-dependent manner, reducing the CV of high intensity points and increasing the CV of low intensity points. This causes the effect of normalisations by sum or optimal alignment on hypothesis testing to depend on the mean of the data tested; for high intensity points, false positives are increased and false negatives are decreased, while for low intensity points, false positives are decreased and false negatives are increased. These results will aid users of Western blotting to choose a suitable normalisation strategy and also understand the implications of this normalisation for subsequent hypothesis testing.
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影响因子：
4.6
作者：
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通讯作者：
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DOI：
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发表时间：
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作者：
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