确定无标记蛋白质组学表达数据归一化和归一化方法最佳组合的统计方法。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Journal of Proteome Research Pub Date : 2025-01-03 Epub Date: 2024-12-10 DOI:10.1021/acs.jproteome.4c00552
Kabilan Sakthivel, Shashi Bhushan Lal, Sudhir Srivastava, Krishna Kumar Chaturvedi, Yasin Jeshima Khan, Dwijesh Chandra Mishra, Sharanbasappa D Madival, Ramasubramanian Vaidhyanathan, Girish Kumar Jha
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引用次数: 0

摘要

无标签的蛋白质组学表达数据集经常表现出数据异质性和缺失值,需要开发有效的归一化和归一化方法。选择适当的归一化和归一化方法本质上是特定于数据的,从可用选项中选择最佳方法对于确保稳健的下游分析至关重要。本研究旨在确定这些方法的最合适组合,以进行质量控制和准确鉴定差异表达蛋白。在本研究中,我们将局部加权线性回归(黄土)、方差稳定归一化(VSN)和鲁棒线性回归(RLR)三种归一化方法与k-近邻(k-NN)、局部最小二乘(LLS)和奇异值分解(SVD)三种归一化方法相结合,形成了9种组合。我们使用统计方法,包括合并变异系数(PCV)、合并方差估计(PEV)和合并中位数绝对偏差(PMAD)来评估组内和组间变异。选择各统计测度对应的最小值组合作为数据集合适的归一化和归一化方法。该方法的性能使用两个钉入的标准无标记蛋白质组学基准数据集进行测试。鉴定的组合返回了较低的NRMSE,并且在鉴定尖刺蛋白方面表现出更好的性能。开发的方法可以通过名为“lfproQC”的R包和用户友好的Shiny web应用程序(https://dabiniasri.shinyapps)访问。io/lfproQC和http://omics.icar.gov.in/lfproQC),使其成为希望将此方法应用于其数据集的研究人员的宝贵资源。
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A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free Proteomics Expression Data.

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set's suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named 'lfproQC' and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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