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matter: an R package for rapid prototyping with larger-than-memory datasets on disk.

Kylie A Bemis1, Olga Vitek1,2

  • 1College of Computer and Information Science, Northeastern University, Boston, MA USA 02115.

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The R package "matter" facilitates direct interaction with large datasets stored across multiple files. This tool supports reproducible research and bioinformatics applications, especially for novel file formats.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Managing and analyzing large-than-memory datasets presents significant computational challenges.
  • Novel and evolving file formats often lack robust software support, hindering data exploration.
  • Reproducible research requires efficient data handling and minimal format conversion.

Purpose of the Study:

  • Introduce 'matter', an R package for seamless interaction with large, multi-file datasets.
  • Provide a flexible tool for data exploration and manipulation, particularly for new file formats.
  • Enhance reproducibility in bioinformatics by reducing data format conversion needs.

Main Methods:

  • Developed the 'matter' R package for direct data access.
  • Implemented functionalities for data exploration and manipulation.
  • Demonstrated 'matter' with the Bioconductor package Cardinal for mass spectrometry imaging data analysis.

Main Results:

  • 'matter' enables direct interaction with larger-than-memory datasets across numerous files.
  • The package supports various data manipulation tasks and is extensible for bioinformatics.
  • Performance illustrated with high-resolution mass spectrometry imaging data analysis.

Conclusions:

  • 'matter' offers a powerful solution for handling large datasets in R, especially with challenging file formats.
  • The package promotes reproducible research practices in bioinformatics.
  • 'matter' is available open-source, fostering wider adoption and development.