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

doi:10.1093/bioinformatics/btac040

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 journal › Article › peer-review

6 Scopus citations

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 language	English
Pages (from-to)	2072-2074
Number of pages	3
Journal	Bioinformatics
Volume	38
Issue number	7
DOIs	https://doi.org/10.1093/bioinformatics/btac040
State	Published - 1 Apr 2022
Externally published	Yes

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btac040

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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.",

author = "Yonghui Dong and Yana Kazachkova and Meng Gou and Liat Morgan and Tal Wachsman and Ehud Gazit and Birkler, {Rune Isak Dupont}",

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AU - Dong, Yonghui

AU - Kazachkova, Yana

AU - Gou, Meng

AU - Morgan, Liat

AU - Wachsman, Tal

AU - Gazit, Ehud

AU - Birkler, Rune Isak Dupont

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AB - 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.

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

Abstract

ASJC Scopus subject areas

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