MALDIrppa: quality control and robust analysis for mass spectrometry data

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Authors Javier PalareaAlbaladejo, Kevin McLean, Frank Wright, David George Emslie Smith Biomathematics and Statistics Scotland, JCMB, The King's Buildings, Peter Guthrie Tait Road, Edinburgh, UK, Proteomics Facility Services, Moredun Research Institute, Pentland Science Park, Bush Loan, Penicuik, Mid Lothian, UK, HeriotWatt University
Journal/Conference Name BIOINFORMATICS
Paper Category
Paper 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:javier.palarea@bioss.ac.uk.
Date of publication 2018
Code Programming Language R
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