title: Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis.
abstract: Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) is a powerful tool for studying dynamic responses of biological systems to different physiological or pathological conditions. Differences in the instrumental response within and between batches introduce unwanted and uncontrolled data variation that should be removed to extract useful information. This work exploits a recently developed method for the identification of batch effects in high throughput genomic data based on the calculation of a δ statistic through principal component analysis (PCA) and guided PCA. Its applicability to LC-MS metabolomic data was tested on two real examples. The first example involved the repeated analysis of 42 plasma samples and 6 blanks in three independent batches, and the second data set involved the analysis of 101 plasma and 18 blank samples in a single batch with a total runtime of 50h. The first and second data set were used to evaluate between and within-batch effects using the δ statistic, respectively. Results obtained showed the usefulness of using the δ statistic together with other approaches such as summary statistics of peak intensity distributions, PCA scores plots or the monitoring of IS peak intensities, to detect and identify instrumental instabilities in LC-MS.
authors.affiliation: Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain.
authors.forename: J
authors.initials: J
authors.lastname: Kuligowski
authors.affiliation: Department of Analytical Chemistry, University of Valencia, Burjassot, Spain.
authors.forename: D
authors.initials: D
authors.lastname: Pérez-Guaita
authors.affiliation: Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain.
authors.forename: I
authors.initials: I
authors.lastname: Lliso
authors.affiliation: Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain.
authors.forename: J
authors.initials: J
authors.lastname: Escobar
authors.affiliation: Analytical Unit, Health Research Institute La Fe, Valencia, Spain.
authors.forename: Z
authors.initials: Z
authors.lastname: León
authors.affiliation: Leitat Technological Center, Bio In Vitro Division, Valencia, Spain.
authors.forename: L
authors.initials: L
authors.lastname: Gombau
authors.affiliation: Department of Pediatric Research, Institute for Surgical Research, Oslo University Hospital - Rikshospitalet, Oslo, Norway.
authors.forename: R
authors.initials: R
authors.lastname: Solberg
authors.affiliation: Department of Pediatric Research, Institute for Surgical Research, Oslo University Hospital - Rikshospitalet, Oslo, Norway.
authors.forename: O D
authors.initials: OD
authors.lastname: Saugstad
authors.affiliation: Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain; Division of Neonatology, University & Polytechnic Hospital La Fe, Valencia, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Vento
authors.affiliation: Leitat Technological Center, Bio In Vitro Division, Valencia, Spain. Electronic address: guillermo.r.quintas@uv.es.
authors.forename: G
authors.initials: G
authors.lastname: Quintás
mesh_terms: D002138:Calibration; D002853:Chromatography, Liquid / Q000379:methods*; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics; D010949:Plasma / Q000737:chemistry*; D025341:Principal Component Analysis / Q000379:methods*; D011786:Quality Control
keywords: Batch effect; Guided principal component analysis; Liquid chromatography-mass spectrometry (LC-MS); Metabolomics