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ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data.

Lisa M Bramer¹, Amanda M White¹, Kelly G Stratton¹

¹Computational Analytics Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America.

Plos Computational Biology

|March 17, 2020

Summary

Fourier transform mass spectrometry (FT-MS) generates complex data. The ftmsRanalysis R package simplifies FT-MS data analysis, offering tools for processing, visualization, and comparison of soil, plant, and aquatic samples.

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Area of Science:

Environmental chemistry
Analytical chemistry
Bioinformatics

Background:

Fourier transform mass spectrometry (FT-MS) offers high resolution and mass accuracy, making it ideal for analyzing complex biological and environmental samples.
Analyzing complex mixtures of compounds in soil, plant, and aquatic samples requires advanced informatics tools.
Understanding carbon compound availability for oxidation and comparing sample chemical properties are key research goals.

Purpose of the Study:

To develop an R package, ftmsRanalysis, for comprehensive analysis of FT-MS data.
To provide tools for data formatting, processing, filtering, and visualization.
To enable flexible and interactive comparison of complex sample datasets.

Main Methods:

Development of the ftmsRanalysis R package with diverse analytical functions.
Integration with the Trelliscope user interface for interactive data visualization.
Application of the package to FT-MS data from a soil microbiology study.

Main Results:

The ftmsRanalysis package offers robust data formatting, processing, and filtering capabilities.
Interactive visualization of complex FT-MS datasets is facilitated through Trelliscope integration.
The package effectively demonstrates core functionalities for sample and group comparisons.

Conclusions:

ftmsRanalysis provides essential informatics tools for researchers utilizing FT-MS.
The package enhances the ability to interpret complex chemical compositions in environmental and biological samples.
Interactive visualizations aid in understanding carbon cycling and sample chemical properties.