| 1. |
- Eriksson, Lennart, et al.
(författare)
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Editorial
- 2007
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 21:10-11, s. 397-
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
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| 2. |
- Gottfries, Johan, et al.
(författare)
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On the impact of uncorrelated variation in regression mathematics
- 2008
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 22:11-12, s. 565-70
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Tidskriftsartikel (refereegranskat)abstract
- The objective of the present study is to investigate if, and if so, how uncorrelated variation relates to regression mathematics as exemplified by partial least squares (PLS) methodology. In contrast to previous methods, orthogonal partial least squares (OPLS) method requires a multi-focus, in the sense that in parallel to calculation of correlation it requires an analysis of orthogonal variation, i.e. the uncorrelated structure in a comprehensive way. Subsequent to the estimation of the correlation is the remaining orthogonal variation, i.e. uncorrelated data, divided into uncorrelated structure and stochastic noise by the OPLS component. Thus, it appears obvious that it is of interest to understand how the uncorrelated variation can influence the interpretation of the regression model. We have scrutinized three examples that pinpoint additional value from OPLS regarding the modelling of the orthogonal, i.e. uncorrelated, variation in regression mathematics. In agreement with the results, we conclude that uncorrelated variations do impact interpretations of regression analyses output and provides not only opportunities by OPLS but also an obligation for the user to maximize benefit from OPLS.
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| 3. |
- Jonsson, Pär, et al.
(författare)
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A strategy for modelling dynamic responses in metabolic samples characterized by GC/MS
- 2006
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 2:3, s. 135-143
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Tidskriftsartikel (refereegranskat)abstract
- A multivariate strategy for studying the metabolic response over time in urinary GC/MS data is presented and exemplified by a study of drug-induced liver toxicity in the rat. The strategy includes the generation of representative data through hierarchical multivariate curve resolution (H-MCR), highlighting the importance of obtaining resolved metabolite profiles for quantification and identification of exogenous (drug related) and endogenous compounds (potential biomarkers) and for allowing reliable comparisons of multiple samples through multivariate projections. Batch modelling was used to monitor and characterize the normal (control) metabolic variation over time as well as to map the dynamic response of the drug treated animals in relation to the control. In this way treatment related metabolic responses over time could be detected and classified as being drug related or being potential biomarkers. In summary the proposed strategy uses the relatively high sensitivity and reproducibility of GC/MS in combination with efficient multivariate curve resolution and data analysis to discover individual markers of drug metabolism and drug toxicity. The presented results imply that the strategy can be of great value in drug toxicity studies for classifying metabolic markers in relation to their dynamic responses as well as for biomarker identification.
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| 4. |
- Jonsson, Pär, et al.
(författare)
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Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data : a potential tool for multi-parametric diagnosis
- 2006
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Ingår i: Journal of Proteome Research. - : American Chemical Society. - 1535-3893 .- 1535-3907. ; 5:6, s. 1407-1414
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Tidskriftsartikel (refereegranskat)abstract
- A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e.g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample (approximately 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.
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| 5. |
- Lundstedt, Torbjörn, et al.
(författare)
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Editorial
- 2006
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 323-324
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Tidskriftsartikel (populärvet., debatt m.m.)
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| 6. |
- Stenlund, Hans, et al.
(författare)
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Unlocking Interpretation in Near Infrared Multivariate Calibrations by Orthogonal Partial Least Squares
- 2009
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Ingår i: Analytical Chemistry. ; 81:1, s. 203-9
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Tidskriftsartikel (refereegranskat)abstract
- Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood’s anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in addition, the Food and Drug Administration (FDA) demands “process understanding” in PAT. Both these issues make OPLS the ideal tool for a multitude of NIR calibrations. In conclusion, OPLS leads to better interpretation of spectrometry data (e.g., NIR) and improved understanding facilitates cross-scientific communication. Such improved knowledge will decrease risk, with respect to both accuracy and precision, when using NIR for PAT applications.
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| 7. |
- Wiklund, Susanne, et al.
(författare)
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Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models
- 2008
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Ingår i: Analytical Chemistry. - Columbus, OH : American Chemical Society. - 0003-2700 .- 1520-6882. ; 80:1, s. 115-22
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Tidskriftsartikel (refereegranskat)abstract
- Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.
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| 8. |
- Aspeborg, Henrik, 1970-, et al.
(författare)
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Vegetabile material, plants and a method of producing a plant having altered lignin properties
- 2008
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Patent (populärvet., debatt m.m.)abstract
- The present invention is related to a set of genes, which when modified in plants gives altered lignin properties. The invention provides DNA construct such as a vector useful in the method of the invention. Further, the invention relates to a plant cell or plant progeny of the plants and wood produced by the plants according to the invention Lower lignin levels will result in improved saccharification for bio-refining and ethanol production and improved pulp and paper. Increased lignin levels will utilise lignin properties for energy production. The genes and DNA constructs may be used for the identification of plants having altered lignin characteristics as compared to the wild-type. According to the invention genes and DNA constructs may also be used as candidate genes in marker assisted breeding.
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| 9. |
- Benedict, Catherine, et al.
(författare)
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Consensus by democracy. Using meta-analyses of microarray and genomic data to model the cold acclimation signaling pathway in Arabidopsis.
- 2006
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Ingår i: Plant Physiology. - : Oxford University Press (OUP). - 0032-0889 .- 1532-2548. ; 141:4, s. 1219-1232
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Tidskriftsartikel (refereegranskat)abstract
- The whole-genome response of Arabidopsis (Arabidopsis thaliana) exposed to different types and durations of abiotic stress has now been described by a wealth of publicly available microarray data. When combined with studies of how gene expression is affected in mutant and transgenic Arabidopsis with altered ability to transduce the low temperature signal, these data can be used to test the interactions between various low temperature-associated transcription factors and their regulons. We quantized a collection of Affymetrix microarray data so that each gene in a particular regulon could vote on whether a cis-element found in its promoter conferred induction (+1), repression (–1), or no transcriptional change (0) during cold stress. By statistically comparing these election results with the voting behavior of all genes on the same gene chip, we verified the bioactivity of novel cis-elements and defined whether they were inductive or repressive. Using in silico mutagenesis we identified functional binding consensus variants for the transcription factors studied. Our results suggest that the previously identified ICEr1 (induction of CBF expression region 1) consensus does not correlate with cold gene induction, while the ICEr3/ICEr4 consensuses identified using our algorithms are present in regulons of genes that were induced coordinate with observed ICE1 transcript accumulation and temporally preceding genes containing the dehydration response element. Statistical analysis of overlap and cis-element enrichment in the ICE1, CBF2, ZAT12, HOS9, and PHYA regulons enabled us to construct a regulatory network supported by multiple lines of evidence that can be used for future hypothesis testing.
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| 10. |
- Bruce, Stephen J, et al.
(författare)
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Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry : From extraction to data analysis
- 2009
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Ingår i: Analytical Biochemistry. - : Elsevier. - 0003-2697 .- 1096-0309. ; 372:2, s. 237-249
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Tidskriftsartikel (refereegranskat)abstract
- The investigation presented here describes a protocol designed to perform high-throughput metabolic profiling analysis on human blood plasma by ultra-performance liquid chromatography/mass spectrometry (UPLC/MS). To address whether a previous extraction protocol for gas chromatography (GC)/MS-based metabolic profiling of plasma could be used for UPLC/MS-based analysis, the original protocol was compared with similar methods for extraction of low-molecular-weight compounds from plasma via protein precipitation. Differences between extraction methods could be observed, but the previously published extraction method was considered the best. UPLC columns with three different stationary phases (C8, C18, and phenyl) were used in identical experimental runs consisting of a total of 60 injections of extracted male and female plasma samples. The C8 column was determined to be the best for metabolic profiling analysis on plasma. The acquired UPLC/MS data of extracted male and female plasma samples was subjected to principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS–DA). Furthermore, a strategy for compound identification was applied here, demonstrating the strength of high-mass-accuracy time-of-flight (TOF)/MS analysis in metabolic profiling.
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