Statistical Methods in Metabolomics

Korman, Alexander; Oh, Amy; Raskind, Alexander; Banks, David

doi:10.1007/978-1-61779-585-5_16

Cited by 30 publications

(28 citation statements)

References 33 publications

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“…Preliminary analysis includes usage of mathematical computing for chromatographic peak retention time alignment between individual analyses. After utilization of this procedure various statistical calculations may be performed in order to quantify the amounts of metabolites, which enables determining the changes of a particular compound in defined conditions (van den Berg et al 2006;Goodacre et al 2007;Korman et al 2012;Weckwerth and Kahl 2013).…”

Section: Instrumentation Applied In Proteomic and Metabolomic Studiesmentioning

confidence: 99%

Influence of abiotic stresses on plant proteome and metabolome changes

et al. 2013

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Plant responses to abiotic stresses are very complex phenomena with individual characteristics for various species. Abiotic stresses (e.g. drought, salinity, flooding, cold, heat, UV radiation, heavy metals, etc.) strongly affect plant growth and development. It is estimated that they are the cause of more than 50 % of crop yield losses. Abiotic stresses are known to activate a multigene response resulting in the changes in various proteins and primary and secondary metabolite accumulation. Therefore, proteomic and metabolomic approaches are becoming very important and powerful tools used in studying plants' reaction to various stimuli. Precise analysis of proteome and metabolome is essential for understanding the fundamentals of stress physiology and biochemistry. In this review, we focus on recent reports concerned to the influence of abiotic stresses on changes in the level of different protein groups and metabolite classes. Basic information about physicochemical methods applied to qualitative and quantitative analyses of biopolymers and natural products is also briefly presented.

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Section: Instrumentation Applied In Proteomic and Metabolomic Studiesmentioning

confidence: 99%

Influence of abiotic stresses on plant proteome and metabolome changes

et al. 2013

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“…Any deviation in how and how long a sample is extracted, quenched, and stored can greatly impact the measured analytes [8,9]. Standards bias represents the effects that different standards have on deriving some absolute or even relative quantification from a measurement.…”

Section: Introductionmentioning

confidence: 99%

“…However, application of this strategy is often specific to the analytical technique and instrumentation. With each sample in its own tube or vial, periodic controls or time-stamped near-random controls (random except to prevent neighbor effects) can be used to track analytical conditions during measurement [9]. For samples handled on plates, Latin square or 2D near-random patterns can be used [9].…”

Section: Introductionmentioning

confidence: 99%

Error Analysis and Propagation in Metabolomics Data Analysis

Moseley

2013

Computational and Structural Biotechnology Journal

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Error analysis plays a fundamental role in describing the uncertainty in experimental results. It has several fundamental uses in metabolomics including experimental design, quality control of experiments, the selection of appropriate statistical methods, and the determination of uncertainty in results. Furthermore, the importance of error analysis has grown with the increasing number, complexity, and heterogeneity of measurements characteristic of ‘omics research. The increase in data complexity is particularly problematic for metabolomics, which has more heterogeneity than other omics technologies due to the much wider range of molecular entities detected and measured. This review introduces the fundamental concepts of error analysis as they apply to a wide range of metabolomics experimental designs and it discusses current methodologies for determining the propagation of uncertainty in appropriate metabolomics data analysis. These methodologies include analytical derivation and approximation techniques, Monte Carlo error analysis, and error analysis in metabolic inverse problems. Current limitations of each methodology with respect to metabolomics data analysis are also discussed.

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“…59,60 Briefly, metabolic profiling is usually performed in a two-step fashion: 1) a combination of gas and/or lipid chromatography is used to separate metabolites followed by 2) mass spectroscopy or nuclear magnetic resonance (NMR) mass spectroscopy for quantification. Untargeted metabolic profiling is designed to measure all metabolites in a given sample, while targeted profiling is designed to identify a fixed subset of metabolites of interest.…”

Section: Metabolic Changes As Biomarkers In Sepsismentioning

confidence: 99%

Metabolism, Metabolomics, and Nutritional Support of Patients with Sepsis

Englert

Rogers

2016

Clinics in Chest Medicine

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Sepsis is characterized by profound metabolic changes including both systemic and cellular alterations that lead to the disruption of normal metabolic homeostasis. These metabolic changes serve as biomarkers for disease severity. Lactate, a metabolite of anaerobic metabolism, is the most widely used ICU biomarker and its use is incorporated into multiple management algorithms. Technological advances now make broader metabolic profiling possible in sepsis, with early studies identifying several metabolic changes associated with sepsis mortality. Finally, given both the marked changes in metabolism in sepsis and the association of worse short- and long-term prognosis in patients with severe metabolic derangements, we summarize the seminal trials conducted to optimize nutrition in the ICU.

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Statistical Methods in Metabolomics

Cited by 30 publications

References 33 publications

Influence of abiotic stresses on plant proteome and metabolome changes

Influence of abiotic stresses on plant proteome and metabolome changes

Error Analysis and Propagation in Metabolomics Data Analysis

Metabolism, Metabolomics, and Nutritional Support of Patients with Sepsis

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