statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data
statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data
复制标题
DOI：
10.1016/j.aca.2018.08.002
复制
发表时间：
2018-12-07
影响因子：
6.2
通讯作者：
Cai, Zongwei
中科院分区：
文献类型：
--
作者：
Luan, Hemi;Ji, Fenfen;Cai, Zongwei
基于大规模定量质谱的代谢组学和蛋白质组学研究需要对多个批次的生物样品进行长期分析，这往往伴随着显着的信号漂移以及各种批次间和批次内的变化。不需要的变化可能导致日间和日内重现性差，这阻碍了发现真正的意义。在质量保证程序中使用质量控制样本和数据处理策略提供了一种评估质量和消除数据分析方差的机制。我们开发的 statTarget 是一款简化的工具，具有易于使用的图形用户界面和一套集成算法，专门用于评估数据质量并消除基于定量质谱的组学数据的不需要的变化。 statTarget 中植入了一种新颖的基于质量控制的随机森林信号校正算法，该算法可以在特征级别消除批次间和批次内不需要的变化。我们基于真实样本的评估表明，开发的算法可以提高基于质谱的代谢组学和蛋白质组学数据的数据精度和统计准确性。此外，statTarget 还提供了数据插补、数据标准化、单变量分析、多变量分析和特征选择的简化程序。总之，statTarget 允许用户友好地提高数据精度以发现生物学差异，这在很大程度上促进了基于定量质谱的组学数据处理和统计分析。 (C) 2018 Elsevier B.V. 保留所有权利。
Large-scale quantitative mass spectrometry-based metabolomics and proteomics study requires the longterm analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter- and intra-batch variations. The unwanted variations can lead to poor inter- and intra-day reproducibility, which is a hindrance to discover real significance. The use of quality control samples and data treatment strategies in the quality assurance procedure provides a mechanism to evaluate the quality and remove the analytical variance of the data. The statTarget we developed is a streamlined tool with an easy-to-use graphical user interface and an integrated suite of algorithms specifically developed for the evaluation of data quality and removal of unwanted variations for quantitative mass spectrometry-based omics data. A novel quality control-based random forest signal correction algorithm, which can remove inter- and intra-batch unwanted variations at feature-level was implanted in the statTarget. Our evaluation based on real samples showed the developed algorithm could improve the data precision and statistical accuracy for mass spectrometry-based metabolomics and proteomics data. Additionally, the statTarget offers the streamlined procedures for data imputation, data normalization, univariate analysis, multivariate analysis, and feature selection. To conclude, the statTarget allows user-friendly the improvement of the data precision for uncovering the biologically differences, which largely facilitates quantitative mass spectrometry-based omics data processing and statistical analysis. (C) 2018 Elsevier B.V. All rights reserved.