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 Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study

2021 ◽  
Author(s):  
Lu Li ◽  
Huub Hoefsloot ◽  
Albert A. Graaf ◽  
Evrim Acar ◽  
Age K. Smilde
Keyword(s):  
Data Analysis ◽  
Individual Variation ◽  
Dynamic Models ◽  
Tensor Factorization ◽  
Experimental Conditions ◽  
Metabolomics Data ◽  
Time Resolved ◽  
Multiway Data Analysis ◽  
Induced Variation ◽  
Underlying Mechanisms

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. In this paper, we study the use of multiway data analysis to reveal the underlying patterns and dynamics in time-resolved metabolomics data. 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.

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.

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 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.

Omaha System Public Health Data: Analysis Methods

2010 ◽  
Vol 58 (2) ◽  
pp. e22-e23
Author(s):  
Karen A. Monsen ◽  
Karen S. Martin ◽  
Bonnie L Westra
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Health Data ◽  
Public Health Data ◽  
Analysis Methods ◽  
Omaha System ◽  
Data Analysis Methods

Peak detection in sediment - charcoal records: impacts of alternative data analysis methods on fire-history interpretations

2010 ◽  
Vol 19 (8) ◽  
pp. 996 ◽  
Author(s):  
Philip E. Higuera ◽  
Daniel G. Gavin ◽  
Patrick J. Bartlein ◽  
Douglas J. Hallett
Keyword(s):  
Data Analysis ◽  
Fire History ◽  
Threshold Value ◽  
Decomposition Methods ◽  
Peak Detection ◽  
Detection Methods ◽  
Statistical Framework ◽  
Analysis Methods ◽  
Decomposition Models ◽  
Data Analysis Methods

Over the past several decades, high-resolution sediment–charcoal records have been increasingly used to reconstruct local fire history. Data analysis methods usually involve a decomposition that detrends a charcoal series and then applies a threshold value to isolate individual peaks, which are interpreted as fire episodes. Despite the proliferation of these studies, methods have evolved largely in the absence of a thorough statistical framework. We describe eight alternative decomposition models (four detrending methods used with two threshold-determination methods) and evaluate their sensitivity to a set of known parameters integrated into simulated charcoal records. Results indicate that the combination of a globally defined threshold with specific detrending methods can produce strongly biased results, depending on whether or not variance in a charcoal record is stationary through time. These biases are largely eliminated by using a locally defined threshold, which adapts to changes in variability throughout a charcoal record. Applying the alternative decomposition methods on three previously published charcoal records largely supports our conclusions from simulated records. We also present a minimum-count test for empirical records, which reduces the likelihood of false positives when charcoal counts are low. We conclude by discussing how to evaluate when peak detection methods are warranted with a given sediment–charcoal record.

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