A Case Report of Switching from Specific Vendor-Based to R-Based Pipelines for Untargeted LC-MS Metabolomics
Metadatos
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Fernández Ochoa, Álvaro; Quirantes Piné, Rosa; Borrás-Linares, Isabel; Cádiz Gurrea, María de la Luz; PRECISESADS Clinical Consortium; Alarcón-Riquelme, Marta E.; Brunius, Carl; Segura-Carretero, AntonioEditorial
MDPI
Materia
metabolomics data pre-processing mass spectrometry
Fecha
2020-01-08Referencia bibliográfica
Fernández Ochoa, A. et. al. Metabolites 2020, 10, 28. [https://doi.org/10.3390/metabo10010028]
Patrocinador
Innovative Medicines Initiative Joint Undertaking under grant agreement No. 115565; European Union’s Seventh Framework Programme (FP7/2007-2013); European Federation of Pharmaceutical Industries and Associations (EFPIA) companiesResumen
Data pre-processing of the LC-MS data is a critical step in untargeted metabolomics
studies in order to achieve correct biological interpretations. Several tools have been developed
for pre-processing, and these can be classified into either commercial or open source software.
This case report aims to compare two specific methodologies, Agilent Profinder vs. R pipeline,
for a metabolomic study with a large number of samples. Specifically, 369 plasma samples were
analyzed by HPLC-ESI-QTOF-MS. The collected data were pre-processed by both methodologies
and later evaluated by several parameters (number of peaks, degree of missingness, quality of
the peaks, degree of misalignments, and robustness in multivariate models). The vendor software
was characterized by ease of use, friendly interface and good quality of the graphs. The open
source methodology could more e ectively correct the drifts due to between and within batch
e ects. In addition, the evaluated statistical methods achieved better classification results with
higher parsimony for the open source methodology, indicating higher data quality. Although both
methodologies have strengths and weaknesses, the open source methodology seems to be more
appropriate for studies with a large number of samples mainly due to its higher capacity and versatility
that allows combining di erent packages, functions, and methods in a single environment.