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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics.

Miranda L Gardner1,2, Michael A Freitas1,2

  • 1Ohio State Biochemistry Program, Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA.

International Journal of Molecular Sciences
|September 10, 2021
PubMed
Summary
This summary is machine-generated.

Accurate proteomics analysis demands effective missing value imputation. This study advocates for hybrid left-censored methods to address both missing at random (MAR) and missing not at random (MNAR) data, crucial for reliable differential abundance findings.

Keywords:
bottom-up proteomicslabel-free quantitationmissing value imputationpeak intensityspectral counting

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Differential abundance analysis in proteomics is challenged by missing data.
  • Missing values can arise from missing at random (MAR) or missing not at random (MNAR) mechanisms.
  • Existing imputation strategies may not adequately address both MAR and MNAR simultaneously.

Purpose of the Study:

  • To evaluate existing missing value imputation strategies for proteomics data.
  • To identify imputation methods that can effectively handle both MAR and MNAR data.
  • To present a case for hybrid left-censored imputation approaches.

Main Methods:

  • Evaluation of various missing value imputation strategies.
  • Focus on methods addressing the combinatorial nature of MAR and MNAR.
  • Analysis of proteomics datasets with varying missing value patterns.

Main Results:

  • Missing value patterns in proteomics data are complex, involving both MAR and MNAR.
  • The rate of MNAR can be influenced by experimental sample treatments.
  • Hybrid left-censored imputation demonstrates potential for handling MNAR.

Conclusions:

  • Appropriate selection of missing value imputation is critical for reliable proteomics analysis.
  • Hybrid left-censored imputation methods offer a promising solution for MNAR challenges.
  • Further adoption of these methods can improve the accuracy of differential abundance studies.