Normalization of Western blot data affects the statistics of estimators^*

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• 作者：Caterina Thomaseth^* ; ^{caterina.thomaseth@ist.uni-stuttgart.de} ; Nicole Radde^* ; ^{nicole.radde@ist.uni-stuttgart.de}
• 关键词：Estimators ; Likelihood function ; Maximum likelihood principle ; Monte Carlo simulation ; Ratio distribution
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：26
• 页码：56-62
• 全文大小：483 K
• 卷排序：49

文摘

This study elaborates on the normalization of data from Western blot experiments and its impact on parameter estimation. Western blot data have to be preprocessed appropriately in order to enable comparison across different replicates. This includes a two step normalization procedure, in which the raw signals are normalized to a loading control and additionally to a reference condition. If the signals themselves are normally distributed, the normalized data are described by ratios of normal distributions, which have some peculiarities that can complicate further analysis such as parameter estimation for biochemical network reconstruction. Here we shortly recapitulate some properties of these ratio distributions and conditions for various approximations that facilitate further analysis. We illustrate results on a case study in which Western blot data are used to infer the fold change in a knockdown experiment.