标题： 中点混合模型与缺失机制（M5）：一种基于似然的质谱蛋白质组学数据量化框架（预印本） 显示英文标题

标题： The Midpoint Mixed Model with a Missingness Mechanism (M5): A Likelihood-Based Framework for Quantification of Mass Spectrometry Proteomics Data (Preprint)

摘要： 统计模型用于蛋白质组学数据，通常将两个样本A和B之间的蛋白质折叠变化估计为来自样本A的平均肽强度除以来自样本B的平均肽强度。 这种平均强度比值未能充分利用实验设计，该设计通过在相同条件下处理来自两个样本的肽来消除未观察到的混杂变量。 通常通过将蛋白质比值估计为匹配肽强度的中位数比值来利用这种结构。 这个简单的解决方案未能考虑导致更复杂的平均强度模型发展的显著缺失数据偏差。 在这里，我们开发了第一个统计模型，在利用样本间肽水平的匹配对的同时考虑不可忽略的缺失性。 我们的模拟分析显示，未能利用肽水平比值的模型会遭受惊人的准确性损失，基本的ANOVA估计的平均MSE比中位数比值估计高371%。 相反，中位数比值估计的平均MSE比我们的模型估计高35%。 对乳腺癌数据的分析证实了这些关系，并表明我们的模型能够将估计的蛋白质数量提高22%。 显示英文摘要

摘要： Statistical models for proteomics data often estimate protein fold changes between two samples, A and B, as the average peptide intensity from sample A divided by the average peptide intensity from sample B. Such average intensity ratios fail to take full advantage of the experimental design which eliminates unseen confounding variables by processing peptides from both samples under identical conditions. Typically this structure is exploited through the estimation of a protein ratio as the median ratio of matched peptide intensities. This simple solution fails to account for a substantial missing data bias which has led to the development of more sophisticated average intensity models. Here we develop the first statistical model that accounts for nonignorable missingness while utilizing peptide level matched pairs across samples. Our simulation analysis shows that models which fail to utilize peptide level ratios, su.er astonishing losses to accuracy with basic ANOVA estimates having an average MSE 371% higher than median ratio estimates. In turn, median ratio estimates have an average MSE 35% higher than our model estimates. An analysis of breast cancer data reinforces these relationships and shows that our model is capable of increasing the number of proteins estimated by 22%.