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.

Quantitative Data Analysis for Information Science Professionals

2020 ◽  
pp. 349-372
Author(s):  
Regis Chireshe
Keyword(s):  
Data Analysis ◽  
Literature Review ◽  
Quantitative Data ◽  
Information Science ◽  
Analysis Software ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Quantitative Data Analysis ◽  
General Aspects ◽  
Data Analysis Methods

The chapter presents general aspects of quantitative data analysis as they relate to information sciences. The chapter is based on a literature review. It begins with explaining the meaning of data and quantitative data. Kinds of quantitative data are presented. The meaning of data analysis and the reasons for data analysis are also discussed. Reasons for quantitative data analysis are also discussed. The ‘what' and ‘why' of statistics in general and for information science researchers in particular is also presented. The chapter also presents the main issues of quantitative data analysis. Steps in quantitative data analysis are also presented. Preparation of quantitative data analysis is followed by a presentation on quantitative data analysis methods. The chapter highlights the popular quantitative data analysis software. A brief presentation on how quantitative data are presented and interpreted is given. The chapter ends with a discussion on the advantages and disadvantages of quantitative data analysis.

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.

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 Risk Analysis Of Onion Farming

2019 ◽  
Vol 1 (1) ◽  
pp. 45-51
Author(s):  
Febri Eugene ◽  
Melli Suryanty ◽  
Nyanyu Neti Arianti
Keyword(s):  
Data Analysis ◽  
Risk Analysis ◽  
Production Costs ◽  
Average Income ◽  
Qualitative And Quantitative ◽  
Analysis Methods ◽  
Average Production ◽  
The Face ◽  
Data Analysis Methods ◽  
Level Of Risk

The purpose of this research are to identify the factors that cause the emergence of risk in onion farming based on the perceptions of farmers, to know the level of production, costs, and income of onion farming, determine the level of risk for production, costs, and income of onion farming, and to analyze onion farmers in the face of production risks. The method of determining the location is purposive method and the method of determining respondents is the census method. Data analysis methods are qualitative and quantitative. The results of the study show the average production of onion is 5,747 Kg / MT / Ha, the average farming cost is Rp. 74,515,798 MT / Ha, and the average income is Rp. 69,868,313 MT / Ha. The level of risk for production, costs, and income is included in the low category.

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