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

28 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

Access to Document

10.1093/bioinformatics/btx628

Cite this

@article{e964612955eb4285ba11accbaa156faf,

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\}",

year = "2018",

month = feb,

doi = "10.1093/bioinformatics/btx628",

language = "English",

volume = "34",

pages = "522–523",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "3",

}

TY - JOUR

T1 - MALDIrppa

T2 - quality control and robust analysis for mass spectrometry data

AU - Palarea-Albaladejo, Javier

AU - McLean, Kevin

AU - Wright, Frank

AU - Smith, David George Emslie

PY - 2018/2

Y1 - 2018/2

N2 - 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].

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].

U2 - 10.1093/bioinformatics/btx628

DO - 10.1093/bioinformatics/btx628

M3 - Article

C2 - 29028890

SN - 1367-4803

VL - 34

SP - 522

EP - 523

JO - Bioinformatics

JF - Bioinformatics

IS - 3

ER -

MALDIrppa: quality control and robust analysis for mass spectrometry data

Abstract

Access to Document

Fingerprint

Cite this