化学
代谢组学
组学
质量(理念)
控制(管理)
计算生物学
生化工程
生物信息学
色谱法
计算机科学
工程类
人工智能
哲学
认识论
生物
作者
Álvaro González‐Domínguez,Núria Estanyol-Torres,Carl Brunius,Rikard Landberg,Raúl González‐Domínguez
标识
DOI:10.1021/acs.analchem.3c03660
摘要
The implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and "out-of-control" observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.
科研通智能强力驱动
Strongly Powered by AbleSci AI