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- Blaise, Benjamin J., et al.
(författare)
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Statistical analysis in metabolic phenotyping
- 2021
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Ingår i: Nature Protocols. - : Nature Publishing Group. - 1754-2189 .- 1750-2799. ; 16:9, s. 4299-4326
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Forskningsöversikt (refereegranskat)abstract
- Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.
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- Kirwan, Gemma M, et al.
(författare)
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Building Multivariate Systems Biology Models
- 2012
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Ingår i: Analytical Chemistry. - Washington : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 84:16, s. 7064-7071
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Tidskriftsartikel (refereegranskat)abstract
- Systems biology methods using large-scale "omics" data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate "omics" data from disparate sources and analyze the results for specific and unique sample correlations. This workflow combines principal component analysis (PCA), orthogonal projections to latent structures discriminate analysis (OPLS-DA), orthogonal 2 projections to latent structures (O2PLS), and shared and unique structures (SUS) plots. The workflow is demonstrated using data from a study in which ApoE3Leiden mice were fed an atherogenic diet consisting of increasing cholesterol levels followed by therapeutic intervention (fenofibrate, rosuvastatin, and LXR activator T-0901317). The levels of structural lipids (lipidomics) and free fatty acids in liver were quantified via liquid chromatography-mass spectrometry (LC-MS). The complementary workflow identified diglycerides as key hepatic metabolites affected by dietary cholesterol and drug intervention. Modeling of the three therapeutics for mice fed a high-cholesterol diet further highlighted diglycerides as metabolites of interest in atherogenesis, suggesting a role in eliciting chronic liver inflammation. In particular, O2PLS-based SUS2 plots showed that treatment with T-0901317 or rosuvastatin returned the diglyceride profile in high-cholesterol-fed mice to that of control animals.
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- Bengtsson, Anders A., et al.
(författare)
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Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren’s Syndrome and Systemic Sclerosis
- 2016
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:7
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Tidskriftsartikel (refereegranskat)abstract
- Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease which can affect most organ systems including skin, joints and the kidney. Clinically, SLE is a heterogeneous disease and shares features of several other rheumatic diseases, in particular primary Sjögrens syndrome (pSS) and systemic sclerosis (SSc), why it is difficult to diag- nose The pathogenesis of SLE is not completely understood, partly due to the heterogeneity of the disease. This study demonstrates that metabolomics can be used as a tool for improved diagnosis of SLE compared to other similar autoimmune diseases. We observed differences in metabolic profiles with a classification specificity above 67% in the comparison of SLE with pSS, SSc and a matched group of healthy individuals. Selected metabolites were also significantly different between studied diseases. Biochemical pathway analysis was conducted to gain understanding of underlying pathways involved in the SLE pathogenesis. We found an increased oxidative activity in SLE, supported by increased xanthine oxidase activity and an increased turnover in the urea cycle. The most discriminatory metabolite observed was tryptophan, with decreased levels in SLE patients compared to control groups. Changes of tryptophan levels were related to changes in the activity of the aromatic amino acid decarboxylase (AADC) and/or to activation of the kynurenine pathway.
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- Biong, A. S., et al.
(författare)
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Intake of dairy fat and dairy products, and risk of myocardial infarction: A case-control study
- 2008
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Ingår i: International Journal of Food Sciences and Nutrition. - : Informa UK Limited. - 0963-7486 .- 1465-3478. ; 59:2, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- The role of dairy fat in the aetiology of myocardial infarction (MI) is controversial. The aim of this study was to evaluate the association between intake of dairy fat and dairy products, and risk of a first acute MI. A total of 111 MI patients with a first acute MI and 107 population controls (men and women, age 45-75 years) were studied. Diet was assessed using a 180-item food frequency questionnaire. The MI cases had higher intake of total fat, but lower intake of saturated fat and dairy fat than the control persons. No effect of dairy fat or saturated fat on the odds ratio for MI was observed, however. A significant inverse trend in odds of MI for intake of cheese was observed, but the trend was no longer significant after adjustment for smoking. The results suggest that intake of fat from dairy products may not be associated with increased risk of having a first MI. The healthy control persons had a diet that differed from the diet of the MI patients in many aspects, and dairy products were a part of this diet. This may have protected them from having a first MI.
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- Bylesjö, Max, et al.
(författare)
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LAMINA : a tool for rapid quantification of leaf size and shape parameters
- 2008
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Ingår i: BMC Plant Biology. - : BMC. - 1471-2229. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Background: An increased understanding of leaf area development is important in a number of fields: in food and non-food crops, for example short rotation forestry as a biofuels feedstock, leaf area is intricately linked to biomass productivity; in paleontology leaf shape characteristics are used to reconstruct paleoclimate history. Such fields require measurement of large collections of leaves, with resulting conclusions being highly influenced by the accuracy of the phenotypic measurement process.Results: We have developed LAMINA (Leaf shApe deterMINAtion), a new tool for the automated analysis of images of leaves. LAMINA has been designed to provide classical indicators of leaf shape (blade dimensions) and size (area), which are typically required for correlation analysis to biomass productivity, as well as measures that indicate asymmetry in leaf shape, leaf serration traits, and measures of herbivory damage (missing leaf area). In order to allow Principal Component Analysis (PCA) to be performed, the location of a chosen number of equally spaced boundary coordinates can optionally be returned.Conclusion: We demonstrate the use of the software on a set of 500 scanned images, each containing multiple leaves, collected from a common garden experiment containing 116 clones of Populus tremula (European trembling aspen) that are being used for association mapping, as well as examples of leaves from other species. We show that the software provides an efficient and accurate means of analysing leaf area in large datasets in an automated or semi-automated work flow.
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- de Sousa, Pedro F. M., 1977-
(författare)
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Chemometrics : Unravelling information from complex data generated by modern instruments
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Chemometrics is a discipline dedicated to solving problems arising from complicated analytical systems, combining statistics, mathematics, and computational programming languages.This thesis is based on the work developed in four scientific projects published as papers in scientific journals. The studies developed in these projects have been essentially focused on a data analysis perspective, interpreting complicated data by means of algorithms, employing chemometrical methodologies. Several chemometrical approaches, based on multivariate data analysis and signal processing algorithms have been studied and employed in each project. Most of the data analysis problems studied these projects are related to liquid chromatography hyphenated to mass spectrometry systems, including tandem mass spectrometry. One of the projects has been related to spectrophotometric data.Chromatographic peak shifts have been attributed to lack of control of the nominal chromatographic parameters. The purpose of the work presented in Paper I was to study retention time data, obtained experimentally by provoking peak shifts with controlled effects, to demonstrate that there are patterns associated with such changing factors affecting chromatographic processes. PCR (Principal Component Regression) models were calculated for each compound (98 compounds), using the retention time data of each compound as responses (y), and the retention time data of the remaining compounds as regressors (X). The results demonstrate that the peak shifts of each compound across samples are correlated with the peak shifts of the other compounds in the chromatographic data. This work confirmed a previous work, where an algorithm was developed to improve alignment of peaks in large number of complex samples, based on peak shift patterns.Partial Least Squares (PLS) is one of the mostly used chemometrics techniques. In the work presented in Paper II, a previously reported modified PLS algorithm was studied. This algorithm was developed with the purpose of not generating overfitting models with increasing noise in X, which happens with the classical PLS. However, the results in less-noisy data were not as good as the classical PLS. From this study, we have developed another modified algorithm that does not overfit with increasing noise in X, and it converges with the solutions of the classical PLS in less-noisy data.DNA adductomics is a recent field in omics that studies modifications in the DNA. The goal of the project in Paper III was to develop a program with a graphical interface to interpret LC-MS/MS using a data independent acquisition method, to identify adducts in DNA nucleosides. The results were compared with those performed manually. The program detected over 150 potential adducts whereas manually, in a previous work, only about 25 were found. This program can detect adducts automatically in a matter of seconds.Cancer has been associated with processes that are related to exposure to pollutants and the consumption of certain food products. This process has been related to electrophilic compounds that react with DNA (adducts). When DNA modifications occur, often defense mechanisms in the cell are triggered often leading to the rupture of the cell. Fragments of DNA (micronuclei) are then roaming in the blood stream. In this work (Paper IV), electrophilic additions to hemoglobin (adducts) and the expression of micronuclei in blood samples from 50 children were studied. One of the goals of the project was to find correlations between the adducts in hemoglobin and the expression of micronuclei. PLS was used to model the data. However, the results were not conclusive (R2 = 0.60), i.e., there may be some trends, but there are other variables not modelled that may influence the variance in expression of micronuclei.
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| 9. |
- Eriksson, Lennart, et al.
(författare)
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Three-block bi-focal PLS (3BIF-PLS) and its application in QSAR
- 2004
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Ingår i: SAR and QSAR in Environmental Research. - : Informa UK Limited. - 1062-936X .- 1029-046X. ; 15:5 & 6, s. 481-99
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Tidskriftsartikel (refereegranskat)abstract
- When X and Y are multivariate, the two-block partial least squares (PLS) method is often used. In this paper, we outline an extension addressing a special case of the three-block (X/Y/Z) problem, where Z sits "under" Y. We have called this approach three-block bi-focal PLS (3BIF-PLS). It views the X/Y relationship as the dominant problem, and seeks to use the additional information in Z in order to improve the interpretation of the Y-part of the X/Y association. Two data sets are used to illustrate 3BIF-PLS. Example I relates to single point mutants of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 and their ability to transform halogenated hydrocarbons, some of which are found as organic pollutants in soil. Example II deals with soil remediation capability of bacteria. Whole bacterial communities are monitored over time using "DNA-fingerprinting" technology to see how pollution affects population composition. Since the data sets are large, hierarchical multivariate modelling is invoked to compress data prior to 3BIF-PLS analysis. It is concluded that the 3BIF-PLS approach works well. The paper contains a discussion of pros and cons of the method, and hints at further developmental opportunities.
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- Galindo-Prieto, Beatriz, 1981-
(författare)
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Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection : VIPOPLS, VIPO2PLS, and MB-VIOP methods
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy.The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models.Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnPLS model interpretability when analyzing a large number of data sets simultaneously (Paper IV).The results of this thesis, and their enclosed papers, showed that VIPOPLS, VIPO2PLS, and MB-VIOP methods successfully assess the most relevant variables for model interpretation in PLS, OPLS, O2PLS, and OnPLS models. In addition, predictability, robustness, dimensionality reduction, and other variable selection purposes, can be potentially improved/achieved by using these methods.
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