Pere Ràfols1,2, Bram Heijs3,4, Esteban Del Castillo1, Oscar Yanes1,2, Liam A McDonnell3,4,5, Jesús Brezmes1,2, Iara Pérez-Taboada2,6, Mario Vallejo2,6, María García-Altares1,2, Xavier Correig1,2. 1. Department of Electronic Engineering, Rovira i Virgili University, IISPV, Tarragona, Spain. 2. Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain. 3. Center for Proteomics & Metabolomics, Leiden University Medical Center, Leiden, The Netherlands. 4. Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands. 5. Fondazione Pisana per la Scienza ONLUS, Pisa, Italy. 6. Instituto de Investigaciones Biomédicas Alberto Sols, Consejo Superior de Investigaciones Científicas (CSIC)/Universidad Autónoma de Madrid, Madrid, Spain.
Abstract
SUMMARY: Mass spectrometry imaging (MSI) can reveal biochemical information directly from a tissue section. MSI generates a large quantity of complex spectral data which is still challenging to translate into relevant biochemical information. Here, we present rMSIproc, an open-source R package that implements a full data processing workflow for MSI experiments performed using TOF or FT-based mass spectrometers. The package provides a novel strategy for spectral alignment and recalibration, which allows to process multiple datasets simultaneously. This enables to perform a confident statistical analysis with multiple datasets from one or several experiments. rMSIproc is designed to work with files larger than the computer memory capacity and the algorithms are implemented using a multi-threading strategy. rMSIproc is a powerful tool able to take full advantage of modern computer systems to completely develop the whole MSI potential. AVAILABILITY AND IMPLEMENTATION: rMSIproc is freely available at https://github.com/prafols/rMSIproc. CONTACT: pere.rafols@urv.cat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Mass spectrometry imaging (MSI) can reveal biochemical information directly from a tissue section. MSI generates a large quantity of complex spectral data which is still challenging to translate into relevant biochemical information. Here, we present rMSIproc, an open-source R package that implements a full data processing workflow for MSI experiments performed using TOF or FT-based mass spectrometers. The package provides a novel strategy for spectral alignment and recalibration, which allows to process multiple datasets simultaneously. This enables to perform a confident statistical analysis with multiple datasets from one or several experiments. rMSIproc is designed to work with files larger than the computer memory capacity and the algorithms are implemented using a multi-threading strategy. rMSIproc is a powerful tool able to take full advantage of modern computer systems to completely develop the whole MSI potential. AVAILABILITY AND IMPLEMENTATION: rMSIproc is freely available at https://github.com/prafols/rMSIproc. CONTACT: pere.rafols@urv.cat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Yonara G Cordeiro; Leandra M Mulder; René J M van Zeijl; Lindsay B Paskoski; Peter van Veelen; Arnoud de Ru; Ricardo F Strefezzi; Bram Heijs; Heidge Fukumasu Journal: Cancers (Basel) Date: 2021-11-24 Impact factor: 6.639
Authors: Stefania-Alexandra Iakab; Gerard Baquer; Marta Lafuente; Maria Pilar Pina; José Luis Ramírez; Pere Ràfols; Xavier Correig-Blanchar; María García-Altares Journal: Anal Chem Date: 2022-02-01 Impact factor: 6.986