User guide

TidyMS2 is an MS data processing library that was created with two goals in mind:

1. provide an easy two use toolkit for processing MS data, allowing users to quickly create data pipelines for entire datasets.

2. provide an extensible framework for implementing new data processing tools, such as processing workflows or algorithms.

To achieve this, TidyMS2 offers a high level API for processing entire MS datasets. This API is built on top of a core API that provides a variety of utilities to model and process MS data. In these introductory guides We will use a bottom-up approach to present the most important components of the low level API and then we will present the high level API for processing entire datasets.

TidyMS2 is built around the idea of a data model which define how entities in a MS data processing pipeline should be represented. In this data model, only a subset of operations are allowed, ensuring a consistent data flow throughout the data pipeline. In these guides, an intuitive introduction to the TidyMS2 data model and data flow is provided. For the interested reader, the architecture overview guide describes the data model in detail.

For users interested in extending TidyMS2, we recommend reading the architecture overview guide after this tutorial followed by the Extending TidyMS2 guide.

To get started, first install TidyMS2 using any of these methods:

Installation

Choose your preferred installation method. We recommend installing TidyMS2 in a virtual environment.

pip

pip install tidyms2

uv

uv pip install tidyms2

You can then continue by reading these topic-specific tutorials. Tutorials can be read in any order, but it is recommended to read them in the order listed below:

• Working with raw data
• Working with chemical data
• Processing individual samples
• Processing multiple samples
• Processing LC-MS datasets
• Simulating data
• Converting raw data to mzML
• Matrix pre-processing