Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies

Lazar, Cosmin; Gatto, Laurent; Ferro, Myriam; Bruley, Christophe; Bürger, Thomas

doi:10.1021/acs.jproteome.5b00981

Public

Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies

Published in Journal of Proteome Research • Feb 23, 2016

Authors:

Cosmin Lazar

Laurent Gatto

Myriam Ferro

Abstract

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Al...

Subject

Imputation (statistics)

Missing data

Computer science

Finding related papers...

Discussions

(0)

No comments yet

Be the first to share your thoughts!