A Method and On-Line Tool for Maximum Likelihood Calibration of Immunoblots and Other Measurements That Are Quantified in Batches.
A Method and On-Line Tool for Maximum Likelihood Calibration of Immunoblots and Other Measurements That Are Quantified in Batches.
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DOI：
10.1371/journal.pone.0149575
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Rutherford S
中科院分区：
文献类型：
--
作者：
Andrews SS;Rutherford S
实验测量需要校准，以将测量信号转换为物理上有意义的值。传统的方法有两个步骤：实验者推导出一个转换函数使用标准的测量，然后校准（或归一化）测量未知样品与此功能。仅从标准测量值推导转换函数会导致结果对实验噪声非常敏感。它还意味着，必须丢弃在没有可靠标准的情况下收集的任何数据。在这里，我们表明，一个“一步校准方法”减少了这些问题的常见情况下，样品进行批量测量，其中一个批次可以是免疫印迹（蛋白质印迹），酶联免疫吸附测定（ELISA），光谱序列，或微阵列，只要一些样品测量重复跨多个批次。一步法根据所有测量迭代计算所有校准结果。它在统计模型的假设下返回样本组成的最可能值，使其成为最大似然预测因子。它对标准品的测量误差不太敏感，并允许使用不包括标准品的某些批次。在直接比较真实的和模拟免疫印迹数据时，1步法始终表现出比常规“2步法”更小的误差。这些结果表明，一步法可能是最有用的情况下，实验者想要分析现有的数据，缺少一些标准的测量和实验者想要提取最好的结果可能从他们的数据。这两种方法的开放源码软件可供下载或在线使用。
Experimental measurements require calibration to transform measured signals into physically meaningful values. The conventional approach has two steps: the experimenter deduces a conversion function using measurements on standards and then calibrates (or normalizes) measurements on unknown samples with this function. The deduction of the conversion function from only the standard measurements causes the results to be quite sensitive to experimental noise. It also implies that any data collected without reliable standards must be discarded. Here we show that a “1-step calibration method” reduces these problems for the common situation in which samples are measured in batches, where a batch could be an immunoblot (Western blot), an enzyme-linked immunosorbent assay (ELISA), a sequence of spectra, or a microarray, provided that some sample measurements are replicated across multiple batches. The 1-step method computes all calibration results iteratively from all measurements. It returns the most probable values for the sample compositions under the assumptions of a statistical model, making them the maximum likelihood predictors. It is less sensitive to measurement error on standards and enables use of some batches that do not include standards. In direct comparison of both real and simulated immunoblot data, the 1-step method consistently exhibited smaller errors than the conventional “2-step” method. These results suggest that the 1-step method is likely to be most useful for cases where experimenters want to analyze existing data that are missing some standard measurements and where experimenters want to extract the best results possible from their data. Open source software for both methods is available for download or on-line use.