Evaluating Proteomics Imputation Methods with Improved Criteria
Evaluating Proteomics Imputation Methods with Improved Criteria
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DOI：
10.1021/acs.jproteome.3c00205
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发表时间：
2023-10-20
影响因子：
4.4
通讯作者：
Noble，William S.
中科院分区：
文献类型：
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作者：
Harris，Lincoln;Fondrie，William E.;Noble，William S.
由串联质谱蛋白质组学实验产生的定量测量通常包含大比例的缺失值。缺失值妨碍了重现性，降低了统计功效，并使其难以在样品或实验之间进行比较。虽然有许多方法可以估算缺失值，但在实践中，最常用的方法是性能最差的方法。此外，以前的基准研究集中在相对简单的测量误差，如均方误差之间的插补和保持了价值。在这里，我们使用三个实用的，“以下游为中心”的标准来评估常用的插补方法的性能。这些标准测量鉴定差异表达的肽、产生新的定量肽和改善肽的定量下限的能力。我们的评估包括几种实验类型和采集策略，包括数据依赖和数据独立的采集。我们发现，插补不一定提高识别差异表达肽的能力，但它可以识别新的定量肽，提高肽的定量下限。我们发现MissForest通常是我们以下游为中心的标准中表现最好的方法。我们还认为，现有的插补方法不正确占肽定量的方差，并强调需要这样做的方法。
Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, “downstream-centric” criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.