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In RawHummus: Raw Data Quality Control Tool for LC-MS System
Welcome to RawHummus 
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 in order to verify system consistency, detect any measurement bias, and establish an appropriate data analysis workflow.
RawHummus is a user-friendly web tool that automates raw data quality control (QC) for metabolomics studies. It generates a detailed QC report with interactive plots, tables, summary statistics, and explanations to help users evaluate the data quality.
In addition, RawHummus enables interactive visualization and inspection of LC-MS instrument log files, allowing users to compare over 40 different instrument metrics, such as ambient temperature and ambient humidity. (currently only Thermo Orbitrap instrument is supported).
Workflow
The use of RawHummus is simple and straightforward. You can follow the user guide in each tab to perform your analysis.
It is worth noting that RawHummus uses the generic file formats, i.e., mzML and mzXML, for QC sample evaluation. This means that raw files must be converted prior to analysis. There are several methods available for converting vendor-specific raw data formats, and you can find more information on this page.
Demo files for RawHummus (including 30 log files and 8 QC files) can be downloaded here.
About RawHummus
RawHummus is publicly available on CRAN repository, with source code available on GitHub under a GPL-3 license.
The web application can be installed in R/RStudio using a simple command install.packages("RawHummus"), and run locally with the command run_app().
Try the RawHummus package in your browser
Any scripts or data that you put into this service are public.
RawHummus documentation built on April 20, 2023, 9:11 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Welcome to RawHummus
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 in order to verify system consistency, detect any measurement bias, and establish an appropriate data analysis workflow.
RawHummus is a user-friendly web tool that automates raw data quality control (QC) for metabolomics studies. It generates a detailed QC report with interactive plots, tables, summary statistics, and explanations to help users evaluate the data quality.
In addition, RawHummus enables interactive visualization and inspection of LC-MS instrument log files, allowing users to compare over 40 different instrument metrics, such as ambient temperature and ambient humidity. (currently only Thermo Orbitrap instrument is supported).
Workflow
The use of RawHummus is simple and straightforward. You can follow the user guide in each tab to perform your analysis.
It is worth noting that RawHummus uses the generic file formats, i.e., mzML and mzXML, for QC sample evaluation. This means that raw files must be converted prior to analysis. There are several methods available for converting vendor-specific raw data formats, and you can find more information on this page.
Demo files for RawHummus (including 30 log files and 8 QC files) can be downloaded here.
About RawHummus
RawHummus is publicly available on CRAN repository, with source code available on GitHub under a GPL-3 license.
The web application can be installed in R/RStudio using a simple command install.packages("RawHummus"), and run locally with the command run_app().
Try the RawHummus package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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