Quickstart

TidyMS [1] is a Python package that provides tools to process and analyze Mass Spectrometry (MS) data. Although suited for general use, it was designed to be used with datasets from LC-HRMS metabolomics experiments. It uses Numpy, Pandas and scikit-learn for data processing and analysis. Some of the functionality that offers is:

• read raw data in the mzML format using tidyms.MSData class, optimized for speed and low memory usage.

• Creation of chromatograms and accumulated spectra from raw data.

• Feature detection and feature correspondence in metabolomics datasets using the tidyms.Assay class.

• Read processed data from other mass spectrometry processing software (XCMS, mzmine2, etc…).

• A container object to manage metabolomics data.

• Data curation of untargeted metabolomics data sets using widely accepted practices from the metabolomics community [2] [3]

• Interactive data visualization using bokeh, or publication quality plots using seaborn.

In the rest of this guide, you can find links for different use cases for the TidyMS package. A basic knowledge of MS and metabolomics is assumed, but you can look up in the Definitions the concepts used in the guides. Installation instructions are available here.

You can refer to the following guides to learn about specific topics:

• Working with raw data

• Processing complete datasets from raw data

• Curation of a metabolomics data matrix

• Feature detection and extraction algorithms

• Feature correspondence algorithm

• Converting proprietary instrument-specific formats into mzML

References

[1]

Riquelme, G. et al, “A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows”. Metabolites 2020, 10, 416. https://doi.org/10.3390/metabo10100416 16, 1, (2015), Pages 104–117, https://doi.org/10.1093/bib/bbt080

[2]

W B Dunn et al, “Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry”, Nature Protocols volume 6, pages 1060–1083 (2011).

[3]

D Broadhurst et al, “Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies.”, Metabolomics, 2018;14(6):72. doi: 10.1007/s11306-018-1367-3