Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis

Kuligowski, Julia; Pérez-Guaita, David; Lliso, Isabel; Escobar, Javier; León, Zacarías; Gombau, Lourdes; Solberg, Rønnaug; Saugstad, Ola Didrik; Vento, Máximo; Quintás, Guillermo

doi:10.1016/j.talanta.2014.07.031

Cited by 29 publications

(23 citation statements)

References 13 publications

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“…Variations between samples can be classified as inter- or intra-batch variations. Reducing inter-batch variations is an important issue in large-scale metabolomics [ 49 , 50 ]. The results show that our metabolomics method controlled inter-batch effects well for most of the measured compounds without any statistical adjustments, regardless of the large scale with 883 QC samples among 105 batches for cations and 946 QC samples among 99 batches for anions.…”

Section: Discussionmentioning

confidence: 99%

Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

et al. 2018

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BackgroundCohort studies with metabolomics data are becoming more widespread, however, large-scale studies involving 10,000s of participants are still limited, especially in Asian populations. Therefore, we started the Tsuruoka Metabolomics Cohort Study enrolling 11,002 community-dwelling adults in Japan, and using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography–mass spectrometry. The CE-MS method is highly amenable to absolute quantification of polar metabolites, however, its reliability for large-scale measurement is unclear. The aim of this study is to examine reproducibility and validity of large-scale CE-MS measurements. In addition, the study presents absolute concentrations of polar metabolites in human plasma, which can be used in future as reference ranges in a Japanese population.MethodsMetabolomic profiling of 8,413 fasting plasma samples were completed using CE-MS, and 94 polar metabolites were structurally identified and quantified. Quality control (QC) samples were injected every ten samples and assessed throughout the analysis. Inter- and intra-batch coefficients of variation of QC and participant samples, and technical intraclass correlation coefficients were estimated. Passing-Bablok regression of plasma concentrations by CE-MS on serum concentrations by standard clinical chemistry assays was conducted for creatinine and uric acid.Results and conclusionsIn QC samples, coefficient of variation was less than 20% for 64 metabolites, and less than 30% for 80 metabolites out of the 94 metabolites. Inter-batch coefficient of variation was less than 20% for 81 metabolites. Estimated technical intraclass correlation coefficient was above 0.75 for 67 metabolites. The slope of Passing-Bablok regression was estimated as 0.97 (95% confidence interval: 0.95, 0.98) for creatinine and 0.95 (0.92, 0.96) for uric acid. Compared to published data from other large cohort measurement platforms, reproducibility of metabolites common to the platforms was similar to or better than in the other studies. These results show that our CE-MS platform is suitable for conducting large-scale epidemiological studies.

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Section: Discussionmentioning

confidence: 99%

Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

et al. 2018

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“…To quantify proteins in biological samples, isobaric tagging (or labeling) is among the most commonly used methods that permit multiplexing of different samples; however, batch effects are common in mass spectrometry in combination with TMT peptide labeling. 23 – 25 The time points from the three TMT sets did not cluster together in the PCA plot, suggesting a batch effect in the TMT experiments ( Fig. 1 ).…”

Section: Discussionmentioning

confidence: 99%

Proteome Profiling of Developing Murine Lens Through Mass Spectrometry

Khan

Ali

Kabir

et al. 2018

Invest. Ophthalmol. Vis. Sci.

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PurposeWe previously completed a comprehensive profile of the mouse lens transcriptome. Here, we investigate the proteome of the mouse lens through mass spectrometry–based protein sequencing at the same embryonic and postnatal time points.MethodsWe extracted mouse lenses at embryonic day 15 (E15) and 18 (E18) and postnatal day 0 (P0), 3 (P3), 6 (P6), and 9 (P9). The lenses from each time point were preserved in three distinct pools to serve as biological replicates for each developmental stage. The total cellular protein was extracted from the lens, digested with trypsin, and labeled with isobaric tandem mass tags (TMT) for three independent TMT experiments.ResultsA total of 5404 proteins were identified in the mouse ocular lens in at least one TMT set, 4244 in two, and 3155 were present in all three TMT sets. The majority of the proteins exhibited steady expression at all six developmental time points; nevertheless, we identified 39 proteins that exhibited an 8-fold differential (higher or lower) expression during the developmental time course compared to their respective levels at E15. The lens proteome is composed of diverse proteins that have distinct biological properties and functional characteristics, including proteins associated with cataractogenesis and autophagy.ConclusionsWe have established a comprehensive profile of the developing murine lens proteome. This repository will be helpful in identifying critical components of lens development and processes essential for the maintenance of its transparency.

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“…For detection of batch effects, visual inspection by principal components analysis (PCA) score plots is often used, although with unsupervised PCA batch effects can easily remain undetected if they are not the largest source of the variation of the data. Recently, a method originally developed for genomics data has been implemented for metabolomics, using calculation of the δ statistic by PCA and guided PCA [61]. Several methods have been developed for the correction of these batch effects, such as statistical models based on scaling factors calculated using complete data sets, e.g.…”

Section: Quality Control and Validationmentioning

confidence: 99%

Optimizing the lipidomics workflow for clinical studies—practical considerations

Hyötyläinen

Orešič

2015

Anal Bioanal Chem

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Lipidomics is increasingly being used in clinical research, offering new opportunities for disease prediction and detection. One of the key challenges of clinical applications of lipidomics is the high sensitivity of measured lipid levels to many analytical, physiological, and environmental factors, which therefore must be taken into account when designing the studies. Here we critically discuss the complete clinical lipidomics workflow, including selection of the subjects, the sample type, the sample preprocessing conditions, and the analytical method and methods for data processing. We also review the lipidomics applications which investigate the confounding factors such as age, gender, fasting time, and handling procedures for measuring blood lipid metabolites.

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Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis

Cited by 29 publications

References 13 publications

Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

Proteome Profiling of Developing Murine Lens Through Mass Spectrometry

Optimizing the lipidomics workflow for clinical studies—practical considerations

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