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Related Concept Videos

Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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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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Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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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 of Amines01:15

Mass Spectrometry of Amines

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In mass spectroscopy, amines undergo fragmentation to give parent ions with odd molecule weights. This observed mass spectrum follows the nitrogen rule; a molecule with an odd number of nitrogen atoms produces a molecular ion with an odd molecular weight. Amines undergo fragmentation through α cleavage, producing nitrogen-containing cations—iminium ions—and alkyl radicals. Mass spectra of aromatic and cyclic aliphatic amines exhibit strong molecular ion peaks, but acyclic...
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Mass Spectrometry: Isotope Effect01:13

Mass Spectrometry: Isotope Effect

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Most elements exist in nature as a mixture of isotopes. The isotopes differ in weight due to their respective number of neutrons. The molecular weight of a molecule is different depending on the specific isotope of its elements involved. As a result, the mass spectrum of the molecule exhibits peaks from the same fragment at multiple positions. The positions of these mass signals depend on the mass differences between isotopes. Furthermore, the intensity of these signals is dependent on the...
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Chemical Ionization (CI) Mass Spectrometry01:21

Chemical Ionization (CI) Mass Spectrometry

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The molecular ion peak of a molecule in the mass spectrum provides vital information for molecular identification. However, conventional electron impact ionization can lead to the rapid dissociation of some molecular ions before they reach the detector. A milder ionization method is required to increase the lifetime of such ionized analyte molecules. Chemical ionization (CI) is a gas-phase protonation reaction useful for mass-analyzing analyte molecules that are easily protonated to yield the...
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Mass Spectrometry: Alkene Fragmentation00:59

Mass Spectrometry: Alkene Fragmentation

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Alkenes lose one electron from the unsaturated π bond upon ionization and form stable molecular ions. Further fragmentation of alkenes occurs through three different reaction pathways. The most prominent fragmentation is the cleavage at the allylic position. The resultant allylic carbocation is resonance stabilized. In the mass spectra of terminal alkenes, this fragment appears at a mass-to-charge ratio of 41. In the internal alkenes, where there are two choices of allylic cleavage, the...
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A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors
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Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.

Runmin Wei1,2, Jingye Wang1, Mingming Su1,3

  • 1University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.

Scientific Reports
|January 14, 2018
PubMed
Summary
This summary is machine-generated.

Handling missing values in mass spectrometry metabolomics is crucial. Random Forest (RF) excelled for missing completely at random/missing at random data, while Quantile Regression Imputation of Left-Censored Data (QRILC) was best for left-censored missing not at random data.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Data Science

Background:

  • Missing values are prevalent in mass spectrometry (MS) metabolomics data.
  • Imputation method selection significantly impacts downstream analyses.
  • Understanding missing value types (MCAR, MAR, MNAR) is critical.

Purpose of the Study:

  • To comprehensively compare eight imputation methods for MS metabolomics data.
  • To evaluate imputation performance across different missing value types.
  • To provide guidance and tools for effective missing value handling.

Main Methods:

  • Comparison of eight imputation methods: zero, HM, mean, median, RF, SVD, kNN, QRILC.
  • Evaluation using NRMSE and SOR for imputation accuracy.
  • Assessment of impact on sample distribution (PCA/PLS-Procrustes) and univariate statistics (t-test, correlation).

Main Results:

  • Random Forest (RF) demonstrated superior performance for MCAR and MAR data.
  • Quantile Regression Imputation of Left-Censored Data (QRILC) was optimal for left-censored MNAR data.
  • Imputation method choice significantly influenced statistical analysis outcomes.

Conclusions:

  • Specific imputation methods are recommended based on missing value type.
  • A comprehensive strategy and web tool (MetImp) are provided for practical application.
  • Accurate imputation is essential for reliable metabolomics data interpretation.