Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

MALDIrppa: quality control and robust analysis for mass spectrometry data.

Javier Palarea-Albaladejo1, Kevin Mclean2, Frank Wright1

  • 1Biomathematics and Statistics Scotland, JCMB, The King's Buildings, Peter Guthrie Tait Road, Edinburgh, EH9 3FD, UK.

Bioinformatics (Oxford, England)
|October 14, 2017
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Beyond the Ventilator: Risk Factors for "Trauma-Associated" Pneumonia in the Trauma Intensive Care Unit.

The Journal of surgical research·2026
Same author

Investigation of a novel assay for the proteomic screening of the secretory and excretory products of individual salmon lice Lepeophtheirus salmonis.

Veterinary parasitology·2026
Same author

A permutation test of differences between externally or internally defined groupings in compositional data sets.

Statistical methods in medical research·2026
Same author

Pathology and parasite distribution in mice challenged with <i>Toxoplasma gondii</i> from different geographical origins.

Parasitology·2026
Same author

Changes in physical activity across retirement: a compositional data analysis approach in a Swedish cohort study.

European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity·2025
Same author

Orthonormal pairwise logratio selection (OPALS) algorithm for compositional data analysis in high dimensions.

Bioinformatics advances·2025

This R package, MALDIrppa, enhances mass spectrometry (MS) data analysis by improving reproducibility. It uses robust statistical methods to filter low-quality spectra and atypical peaks, ensuring reliable results.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Analytical Chemistry

Background:

  • Mass spectrometry (MS) data analysis faces challenges with reproducibility.
  • Deviating signals can negatively impact data pre-processing and downstream analysis.
  • Existing computational tools may not fully address these issues in high-throughput data.

Purpose of the Study:

  • To introduce MALDIrppa, an R package for robust mass spectrometry data analysis.
  • To alleviate reproducibility issues in MS data pre-processing and analysis.
  • To provide tools for identifying and filtering low-quality mass spectra and atypical peak profiles.

Main Methods:

  • Implementation of robust statistical methods within an R package.
  • Development of algorithms for identifying and filtering low-quality mass spectra.

Related Experiment Videos

  • Creation of functions for detecting and handling atypical peak profiles.
  • Integration of monitoring and data handling capabilities for pre-processing.

    • Main Results:

    • MALDIrppa facilitates the identification and filtering of low-quality mass spectra.
    • The package effectively addresses atypical peak profiles in MS data.
    • It enhances the robustness of data pre-processing and downstream analysis.
    • The tool extends existing computational capabilities for high-throughput MS data.

    Conclusions:

    • MALDIrppa offers a robust approach to mass spectrometry data analysis.
    • The package improves data quality and analytical reproducibility.
    • It is a valuable extension for computational tools in high-throughput data analysis.