Modelling and simulation for metabolomics data analysis

2005 ◽  
Vol 33 (6) ◽  
pp. 1427-1429 ◽  
Author(s):  
P. Mendes ◽  
D. Camacho ◽  
A. de la Fuente
Keyword(s):  
Data Analysis ◽  
Synthetic Data ◽  
Network Models ◽  
Large Data ◽  
Biochemical Network ◽  
Data Sets ◽  
Metabolomics Data ◽  
Analysis Methods ◽  
True Knowledge ◽  
Data Analysis Methods

The advent of large data sets, such as those produced in metabolomics, presents a considerable challenge in terms of their interpretation. Several mathematical and statistical methods have been proposed to analyse these data, and new ones continue to appear. However, these methods often disagree in their analyses, and their results are hard to interpret. A major contributing factor for the difficulties in interpreting these data lies in the data analysis methods themselves, which have not been thoroughly studied under controlled conditions. We have been producing synthetic data sets by simulation of realistic biochemical network models with the purpose of comparing data analysis methods. Because we have full knowledge of the underlying ‘biochemistry’ of these models, we are better able to judge how well the analyses reflect true knowledge about the system. Another advantage is that the level of noise in these data is under our control and this allows for studying how the inferences are degraded by noise. Using such a framework, we have studied the extent to which correlation analysis of metabolomics data sets is capable of recovering features of the biochemical system. We were able to identify four major metabolic regulatory configurations that result in strong metabolite correlations. This example demonstrates the utility of biochemical simulation in the analysis of metabolomics data.

scholarly journals Analysis of ionospheric electrodynamic parameters on mesoscales – a review of selected techniques using data from ground-based observation networks and satellites

2011 ◽  
Vol 29 (3) ◽  
pp. 467-491 ◽  
Author(s):  
H. Vanhamäki ◽  
O. Amm
Keyword(s):  
Data Analysis ◽  
Data Sets ◽  
Practical Applications ◽  
Analysis Methods ◽  
Ionospheric Electrodynamics ◽  
Simplifying Assumptions ◽  
Using Data ◽  
Measured Input ◽  
Data Analysis Methods ◽  
Selection Of

Abstract. We present a review of selected data-analysis methods that are frequently applied in studies of ionospheric electrodynamics and magnetosphere-ionosphere coupling using ground-based and space-based data sets. Our focus is on methods that are data driven (not simulations or statistical models) and can be used in mesoscale studies, where the analysis area is typically some hundreds or thousands of km across. The selection of reviewed methods is such that most combinations of measured input data (electric field, conductances, magnetic field and currents) that occur in practical applications are covered. The techniques are used to solve the unmeasured parameters from Ohm's law and Maxwell's equations, possibly with help of some simplifying assumptions. In addition to reviewing existing data-analysis methods, we also briefly discuss possible extensions that may be used for upcoming data sets.

Unsupervised Data Analysis Methods used in Qualitative and Quantitative Metabolomics and Metabonomics

2011 ◽  
pp. 1-28
Author(s):  
Miroslava Cuperlovic-Culf
Keyword(s):  
Data Analysis ◽  
Large Set ◽  
High Throughput Analysis ◽  
Metabolomics Data ◽  
Qualitative And Quantitative ◽  
Quantitative Metabolomics ◽  
Advantages And Disadvantages ◽  
Unsupervised Analysis ◽  
Analysis Methods ◽  
Data Analysis Methods

Metabolomics or metababonomics is one of the major high throughput analysis methods that endeavors holistic measurement of metabolic profiles of biological systems. Data analysis approaches in metabolomics can broadly be divided into qualitative – analysis of spectral data and quantitative – analysis of individual metabolite concentrations. In this work, the author will demonstrate the benefits and limitations of different unsupervised analysis tools currently utilized in qualitative and quantitative metabolomics data analysis. Following a detailed literature review outlining different applications of unsupervised methods in metabolomics, the author shows examples of an application of the major previously utilized unsupervised analysis methods. The testing of these methods was performed using qualitative as well as corresponding quantitative metabolite data derived to represent a large set of 2,000 objects. Spectra of mixtures were obtained from different combinations of experimental NMR measurements of 13 prevalent metabolites at five different groups of concentrations representing different phenotypes. The analysis shows advantages and disadvantages of standard tools when applied specifically to metabolomics.

scholarly journals Exploring dynamic metabolomics data with multiway data analysis: a simulation study

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Lu Li ◽  
Huub Hoefsloot ◽  
Albert A. de Graaf ◽  
Evrim Acar ◽  
Age K. Smilde
Keyword(s):  
Data Analysis ◽  
Individual Variation ◽  
Simulated Data ◽  
Ground Truth ◽  
Metabolomics Data ◽  
Analysis Methods ◽  
Multiway Data Analysis ◽  
Induced Variation ◽  
Underlying Mechanisms ◽  
Data Analysis Methods

Abstract Background Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied. Results We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. Conclusion Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

scholarly journals USAGE OF ADVANCED DATA ANALYSIS IN AUSTRIAN INDUSTRIAL COMPANIES

2021 ◽  
Vol 2 ◽  
pp. 125-131
Author(s):  
Martin Wallner ◽  
Tomáš Peráček
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Data ◽  
Complex Data ◽  
Small Companies ◽  
High Turnover ◽  
Industrial Companies ◽  
Analysis Methods ◽  
It Department ◽  
Data Analysis Methods

Data has become one of the most valuable resources for companies. The large data volumes of Big Data projects allow institutions the application of various data analysis methods. Compared to older analysis methods, which mostly have an informative function, predictive and prescriptive analysis methods allow foresight and the prevention of future problems and errors. This paper evaluates the current state of advanced data analysis in Austrian industrial companies. Furthermore, it investigates if the advantages of complex data analyses can be monetarized and if cooperate figures such as the turnover or company size influence the answers of the survey. For that reason, a survey among industrial companies in Austria was performed to assess the usage of complex data analysis methods and Big Data. It is shown that small companies use descriptive and diagnostic analysis methods, while big companies use more advanced analytical methods. Companies with a high turnover are also more likely to perform Big Data projects. On an international comparison for most Austrian industrial companies, Big Data is not the main focus of their IT department. Also, modern data architectures are not as extensively implemented as in other countries of the DACH region. However, there is a clear perception by Austrian industrial companies that forward-looking data analysis methods will be predominant in five years.

scholarly journals Exploratory data analysis on large data sets: The example of salary variation in Spanish Social Security Data

2020 ◽  
pp. 234094442095733
Author(s):  
Catia Nicodemo ◽  
Albert Satorra
Keyword(s):  
Data Analysis ◽  
Social Security ◽  
Gender Gap ◽  
Exploratory Data Analysis ◽  
Large Data ◽  
Jel Classification ◽  
Small Samples ◽  
Analysis Methods ◽  
Exploratory Data ◽  
Data Analysis Methods

New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great potential to facilitate group comparison in the case of an extensive sample. This article presents exploratory data analysis methods useful for inspecting variation across groups in crucial variables and detecting heterogeneity. The exploratory data analysis methods (introduced by Tukey in his seminal book of 1977) encompass a set of statistical tools aimed to extract information from data using simple graphical tools. In this article, some of the exploratory data analysis methods like the boxplot and scatterplot are revisited and enhanced using modern graphical computational devices (as, for example, the heat-map) and their use illustrated with Spanish Social Security data. We explore how earnings vary across several factors like age, gender, type of occupation, and contract, and in particular, the gender gap in salaries is visualized in various dimensions relating to the type of occupation. The exploratory data analysis methods are also applied to assessing and refining competing regressions by plotting residuals-versus-fitted values. The methods discussed should be useful to researchers to assess heterogeneity in data, across-group variation, and classical diagnostic plots of residuals from alternative models fits. JEL CLASSIFICATION: C55; J01; J08; Y10; C80

scholarly journals Objective assessment of SERS thin films: comparison of silver on copper via galvanic displacement with commercially available fabricated substrates

2017 ◽  
Vol 9 (33) ◽  
pp. 4783-4789 ◽  
Author(s):  
Samuel Mabbott ◽  
Yun Xu ◽  
Royston Goodacre
Keyword(s):  
Thin Films ◽  
Data Analysis ◽  
Objective Assessment ◽  
Galvanic Displacement ◽  
Sers Signal ◽  
Analysis Methods ◽  
Data Analysis Methods

Reproducibility of SERS signal acquired from thin films developed in-house and commercially has been assessed using seven data analysis methods.

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