Jove
Visualize
Contact Us

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

RegVelo: Gene-regulatory-informed dynamics of single cells.

Cell·2026
Same author

Delineating the copy-number substructure of metastatic tumors with CopyKit.

Molecular cell·2026
Same author

Thermal Management Technologies for Improving the Thermal Stability of Perovskite Solar Cells.

Nano-micro letters·2026
Same author

Dictionary of human intestinal organoid responses to secreted niche factors at single cell resolution.

Nature communications·2026
Same author

A non-ionic fluorinated p-dopant enables the construction of efficient and stable perovskite solar cells.

Chemical science·2025
Same author

Enoxaparin Improves Outcomes in Cerebral Infarction Rats by Reducing Microcirculatory Thrombosis and Hypoperfusion.

Journal of neuroscience research·2025
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 Experiment Video

Updated: Aug 23, 2025

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry
10:05

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Published on: October 24, 2018

9.5K

Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with GSimp.

Runmin Wei1, Jingye Wang2

  • 1The University of Texas MD Anderson Cancer Center, Department of Genetics, Houston, TX, USA. rwei2@mdanderson.org.

Methods in Molecular Biology (Clifton, N.J.)
|October 29, 2022
PubMed
Summary

Mass spectrometry (MS)-based omics studies often have missing values due to detection limits, which can bias results. GSimp, a new Gibbs sampler method, effectively imputes these missing values in MS-proteomics data.

Keywords:
Gibbs samplerImputationLeft censorMass spectrometryMissing not at randomProteomics

More Related Videos

A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis
09:40

A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis

Published on: April 28, 2022

2.5K
Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS
17:12

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS

Published on: December 20, 2010

15.7K

Related Experiment Videos

Last Updated: Aug 23, 2025

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry
10:05

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Published on: October 24, 2018

9.5K
A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis
09:40

A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis

Published on: April 28, 2022

2.5K
Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS
17:12

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS

Published on: December 20, 2010

15.7K

Area of Science:

  • Biochemistry
  • Proteomics
  • Bioinformatics

Background:

  • Missing values are common in mass spectrometry (MS)-based omics, particularly proteomics.
  • Values below the limit of detection or quantification (LOD/LOQ) are often missing not at random (MNAR).
  • MNAR data can cause biased statistical analysis and hinder downstream applications.

Purpose of the Study:

  • To introduce GSimp, a novel imputation method for MS-proteomics data.
  • To address the challenge of missing not at random (MNAR) values in omics studies.
  • To provide a tool for accurate statistical estimation in MS-based omics.

Main Methods:

  • Development of GSimp, a Gibbs sampler-based imputation approach.
  • Focus on imputing left-censored missing values specific to MS-proteomics.
  • Detailed explanation of MNAR principles and GSimp's application.

Main Results:

  • GSimp effectively handles left-censored missing values in MS-proteomics datasets.
  • The method is designed to mitigate bias caused by MNAR data.
  • Facilitates more reliable downstream analyses in omics research.

Conclusions:

  • GSimp offers a robust solution for imputing MNAR values in MS-proteomics.
  • Accurate imputation is crucial for unbiased statistical inference in omics.
  • This approach supports the advancement of MS-based omics studies.