基于质谱的无标记定量蛋白质组学差异分析中多重插补引起的变异性
Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics
摘要：
填补缺失值是无标记定量蛋白质组学中的常见做法。插补的目的是用用户定义的值替换缺失值。然而，在插补过程的下游，插补本身可能不会得到最佳考虑，因为插补数据集通常被认为是完整的。因此，没有充分考虑到归因于插补的不确定性。我们提供了一个严格的多重插补策略，导致一个较小的偏差估计的参数的变异性感谢鲁宾的规则。然后使用贝叶斯分层模型调节基于插补的肽强度的方差估计。该估计量最后被包括在调节t-检验统计量中以提供差异分析结果。此工作流程可用于定量数据集中的肽和蛋白质水平。对于基于肽水平定量数据的蛋白质水平结果，还包括聚集步骤。我们的方法，命名为mi 4p，比较了最先进的limma工作流程中实现的DAPAR R包，无论是在模拟和真实的数据集。我们观察到灵敏度和特异性之间的权衡，而mi 4p的整体性能在F评分方面优于DAPAR。
英文摘要：
Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters' variability thanks to Rubin's rules. The imputation-based peptide's intensities' variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can be used both at peptide and protein-level in quantification datasets. For protein-level results based on peptide-level quantification data, an aggregation step is also included. Our methodology, named mi4p, was compared to the state-of-the-art limma workflow implemented in the DAPAR R package, both on simulated and real datasets. We observed a trade-off between sensitivity and specificity, while the overall performance of mi4p outperforms DAPAR in terms of F-Score.
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