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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
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Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
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Constructing Metabolic Association Networks Using High-dimensional Mass Spectrometry Data.

Imhoi Koo1, Xiaoli Wei1, Xue Shi1

  • 1Department of Chemistry, University of Louisville, Louisville, KY 40292, USA.

Chemometrics and Intelligent Laboratory Systems : an International Journal Sponsored by the Chemometrics Society
|November 22, 2014
PubMed
Summary

This study compares methods for building metabolic association networks. Principle Component Regression (PCR) and Independent Component Regression (ICR) show superior stability and performance, especially for complex networks, outperforming other methods in both simulations and real metabolomics data analysis.

Keywords:
Gaussian graphical modelMetabolomicsextrinsic similarityindependent component regressionpartial correlationpartial least squares regressionprinciple component regression

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Area of Science:

  • Metabolomics and Systems Biology
  • Bioinformatics and Computational Biology

Background:

  • Metabolic association networks are crucial for understanding cellular molecular mechanisms and metabolite functions.
  • Gaussian Graphical Models (GGMs) are widely used in genomics for biological network inference, but their performance in metabolomics remains unevaluated.
  • The complexity of metabolic networks, where variables often exceed sample size, presents unique challenges for network construction.

Purpose of the Study:

  • To evaluate and compare the performance of various Gaussian Graphical Model-based methods for constructing metabolic association networks.
  • To identify the most stable and accurate methods for inferring metabolic networks, particularly under conditions where the number of variables exceeds the sample size.

Main Methods:

  • Compared Principle Component Regression (PCR), Independent Component Regression (ICR), Shrinkage Covariance Estimate (SCE), Partial Least Squares Regression (PLSR), and Extrinsic Similarity (ES) methods.
  • Evaluated methods using simulation studies by varying sample size and network density (complexity).
  • Applied the most promising methods to experimental metabolomics data from mouse liver extracts.

Main Results:

  • In simulations, PCR and ICR demonstrated greater stability against variations in sample size and network density compared to SCE and PLSR, particularly in terms of F1 scores.
  • PCR and ICR outperformed other methods in high-density network simulations, while PLSR and SCE were better suited for low-density networks.
  • Analysis of mouse liver metabolomics data revealed that PCR and ICR identified more significant metabolic network edges, validated by KEGG pathway analysis, compared to PLSR and SCE.

Conclusions:

  • Principle Component Regression (PCR) and Independent Component Regression (ICR) are more robust and effective methods for constructing metabolic association networks than SCE and PLSR, especially for complex biological systems.
  • Metabolic networks are generally more complex than genomic networks, favoring the performance of PCR and ICR.
  • The findings provide valuable insights for selecting appropriate computational methods in metabolomics research for accurate biological network inference.