MALDIrppa: quality control and robust analysis for mass spectrometry data

Javier Palarea-Albaladejo; Kevin McLean; Frank Wright; David George Emslie Smith

doi:10.1093/bioinformatics/btx628

MALDIrppa: quality control and robust analysis for mass spectrometry data

Javier Palarea-Albaladejo, Kevin McLean, Frank Wright, David George Emslie Smith

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

Summary: This R package helps to implement a robust approach to deal with mass spectrometry (MS) data. It is aimed at alleviating reproducibility issues and pernicious effects of deviating signals on both data pre-processing and downstream data analysis. Based on robust statistical methods, it facilitates the identification and filtering of low-quality mass spectra and atypical peak profiles, as well as monitoring and data handling through pre-processing, which extends existing computational tools for high-throughput data.

Availability and implementation: MALDIrppa is implemented as a package for the R environment for data analysis and it is freely available to download from the CRAN repository at https://CRAN.R-project.org/package=MALDIrppa.

Contact: [email protected].

Original language	English
Pages (from-to)	522–523
Number of pages	2
Journal	Bioinformatics
Volume	34
Issue number	3
Early online date	4 Oct 2017
DOIs	https://doi.org/10.1093/bioinformatics/btx628
Publication status	Published - Feb 2018

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title = "MALDIrppa: quality control and robust analysis for mass spectrometry data",

abstract = "Summary: This R package helps to implement a robust approach to deal with mass spectrometry (MS) data. It is aimed at alleviating reproducibility issues and pernicious effects of deviating signals on both data pre-processing and downstream data analysis. Based on robust statistical methods, it facilitates the identification and filtering of low-quality mass spectra and atypical peak profiles, as well as monitoring and data handling through pre-processing, which extends existing computational tools for high-throughput data.Availability and implementation: MALDIrppa is implemented as a package for the R environment for data analysis and it is freely available to download from the CRAN repository at https://CRAN.R-project.org/package=MALDIrppa.Contact: [email protected].",

author = "Javier Palarea-Albaladejo and Kevin McLean and Frank Wright and Smith, {David George Emslie}",

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AB - Summary: This R package helps to implement a robust approach to deal with mass spectrometry (MS) data. It is aimed at alleviating reproducibility issues and pernicious effects of deviating signals on both data pre-processing and downstream data analysis. Based on robust statistical methods, it facilitates the identification and filtering of low-quality mass spectra and atypical peak profiles, as well as monitoring and data handling through pre-processing, which extends existing computational tools for high-throughput data.Availability and implementation: MALDIrppa is implemented as a package for the R environment for data analysis and it is freely available to download from the CRAN repository at https://CRAN.R-project.org/package=MALDIrppa.Contact: [email protected].

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MALDIrppa: quality control and robust analysis for mass spectrometry data

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