Dong Yonghui, Kazachkova Yana, Gou Meng, Morgan Liat, Wachsman Tal, Gazit Ehud, Birkler Rune Isak Dupont
Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel.
Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
Bioinformatics. 2022 Mar 28;38(7):2072-2074. doi: 10.1093/bioinformatics/btac040.
Robust and reproducible data is essential to ensure high-quality analytical results and is particularly important for large-scale metabolomics studies where detector sensitivity drifts, retention time and mass accuracy shifts frequently occur. Therefore, raw data need to be inspected before data processing to detect measurement bias and verify system consistency.
Here, we present RawHummus, an R Shiny app for an automated raw data quality control (QC) in metabolomics studies. It produces a comprehensive QC report, which contains interactive plots and tables, summary statistics and detailed explanations. The versatility and limitations of RawHummus are tested with 13 metabolomics/lipidomics datasets and 1 proteomics dataset obtained from 5 different liquid chromatography mass spectrometry platforms.
RawHummus is released on CRAN repository (https://cran.r-project.org/web/packages/RawHummus), with source code being available on GitHub (https://github.com/YonghuiDong/RawHummus). The web application can be executed locally from the R console using the command 'runGui()'. Alternatively, it can be freely accessed at https://bcdd.shinyapps.io/RawHummus/.
Supplementary data are available at Bioinformatics online.
可靠且可重复的数据对于确保高质量的分析结果至关重要，对于大规模代谢组学研究尤为重要，因为在这类研究中经常会出现检测器灵敏度漂移、保留时间和质量精度偏移的情况。因此，在数据处理之前需要检查原始数据，以检测测量偏差并验证系统一致性。
在此，我们展示了RawHummus，这是一个用于代谢组学研究中自动原始数据质量控制（QC）的R Shiny应用程序。它会生成一份全面的QC报告，其中包含交互式图表和表格、汇总统计数据以及详细解释。我们使用从5个不同液相色谱质谱平台获得的13个代谢组学/脂质组学数据集和1个蛋白质组学数据集对RawHummus的通用性和局限性进行了测试。
补充数据可在《生物信息学》在线获取。
