RawHummus: An R Shiny app for automated raw data quality control in metabolomics

Yonghui Dong, Yana Kazachkova, Meng Gou, Liat Morgan, Tal Wachsman, Ehud Gazit, Rune Isak Dupont Birkler

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)2072-2074
Number of pages3
JournalBioinformatics
Volume38
Issue number7
DOIs
StatePublished - 1 Apr 2022

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