Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Patient-derived tumor organoids for personalized cancer immunotherapy: An immunopeptidome-to-validation approach in RCC and BC.

Molecular therapy. Oncology·2026
Same author

Proteomics-based approach reveals the involvement of spliceosomal components SF3B and SerpinB9 in dermatofibrosarcoma protuberans.

Orphanet journal of rare diseases·2026
Same author

Towards an ecosystem of clinical decision support tools for precision cancer medicine.

NPJ precision oncology·2026
Same author

Mass spectrometry-based proteomics delivers in-depth proteome profiling of FFPE lung cancer biopsies from single glass slides.

NPJ precision oncology·2026
Same author

Quality variation patterns in reclaimed pedunculated filament backfill solution during storage.

Frontiers in plant science·2026
Same author

Homozygous loss-of-function mutation in SIT1 leads to combined immunodeficiency due to dysregulated T-cell receptor signaling.

The Journal of allergy and clinical immunology·2026

Related Experiment Video

Updated: Dec 25, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

40.6K

DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis.

Yafeng Zhu1, Lukas M Orre1, Yan Zhou Tran1

  • 1Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.

Molecular & Cellular Proteomics : MCP
|March 25, 2020
PubMed
Summary
This summary is machine-generated.

DEqMS is a new statistical method for analyzing quantitative proteomics data from mass spectrometry. It accurately detects differential protein expression by accounting for data structure, improving results over existing methods.

Keywords:
FDRQuantificationTMTdata evaluationdifferential analysislabel-free quantificationmass spectrometryquality control and metricsstatistics

More Related Videos

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

12.5K
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

34.0K

Related Experiment Videos

Last Updated: Dec 25, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

40.6K
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

12.5K
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

34.0K

Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Statistical Bioinformatics

Background:

  • Quantitative proteomics using mass spectrometry is vital for biomarker discovery and biological research.
  • Current statistical methods for analyzing differential protein expression lack standardization and do not fully account for mass spectrometry data characteristics.
  • Existing approaches often involve compromises in statistical power, applicability, and user-friendliness.

Purpose of the Study:

  • To introduce DEqMS, a robust statistical method specifically designed for differential protein expression analysis in mass spectrometry data.
  • To address the limitations of existing methods by incorporating the specific data structure of mass spectrometry.
  • To improve the accuracy and reliability of identifying differentially expressed proteins.

Main Methods:

  • Developed DEqMS, a novel statistical approach tailored for mass spectrometry-based quantitative proteomics.
  • DEqMS accounts for the dependence of variance on the number of PSMs or peptides used for protein quantification.
  • Evaluated DEqMS using various datasets, including E. coli proteome spike-in data, with both label-free and TMT-labeled quantification.

Main Results:

  • DEqMS demonstrated a more accurate estimation of protein variance by considering the number of peptides/PSMs.
  • The method successfully included single peptide identifications without inflating false discovery rates.
  • DEqMS consistently outperformed existing statistical methods in accuracy for detecting altered protein levels across different quantification strategies.

Conclusions:

  • DEqMS provides a more accurate and robust statistical framework for differential protein expression analysis in quantitative proteomics.
  • The method enhances the reliability of findings from mass spectrometry-based studies.
  • DEqMS is available as an R package, facilitating its adoption in the research community.