LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform t...

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Published in:Journal of proteome research Vol. 22; no. 6; p. 2114
Main Authors: Bai, Mingze, Deng, Jingwen, Dai, Chengxin, Pfeuffer, Julianus, Sachsenberg, Timo, Perez-Riverol, Yasset
Format: Journal Article
Language:English
Published: United States 02.06.2023
Subjects:
Mass Spectrometry - methods
Peptides - analysis
Proteins - analysis
Proteomics - methods
Software
protein fold changes
imputation and normalization algorithms
data analysis
mass spectrometry proteomics
protein expression
ISSN:1535-3907, 1535-3907
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Abstract Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.
AbstractList Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.
Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.
Author Bai, Mingze
Pfeuffer, Julianus
Dai, Chengxin
Sachsenberg, Timo
Deng, Jingwen
Perez-Riverol, Yasset
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  givenname: Mingze
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  organization: State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing 102206, China
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  givenname: Jingwen
  surname: Deng
  fullname: Deng, Jingwen
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  givenname: Chengxin
  surname: Dai
  fullname: Dai, Chengxin
  organization: State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing 102206, China
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  surname: Pfeuffer
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  surname: Perez-Riverol
  fullname: Perez-Riverol, Yasset
  organization: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hixton, Cambridge CB10 1SD, United Kingdom
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Keywords protein fold changes
imputation and normalization algorithms
data analysis
mass spectrometry proteomics
protein expression
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SubjectTerms Mass Spectrometry - methods
Peptides - analysis
Proteins - analysis
Proteomics - methods
Software
Title LFQ-Based Peptide and Protein Intensity Differential Expression Analysis
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