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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
RawHummus: an R Shiny app for automated raw data quality control in metabolomics
Yonghui Dong1, Yana Kazachkova2, Meng Gou3
1Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel.
Motivation:
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.
Results:
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.
Availability And Implementation:
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 Information:
Supplementary data are available at Bioinformatics online.
