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

Runmin Wei; Jingye Wang

doi:10.1007/978-1-0716-1967-4_6

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

Methods Mol Biol. 2023:2426:119-129. doi: 10.1007/978-1-0716-1967-4_6.

Authors

Runmin Wei¹, Jingye Wang²

Affiliations

¹ The University of Texas MD Anderson Cancer Center, Department of Genetics, Houston, TX, USA. rwei2@mdanderson.org.
² , Houston, TX, USA.

PMID: 36308687
DOI: 10.1007/978-1-0716-1967-4_6

Abstract

Missing values caused by the limit of detection or quantification (LOD/LOQ) were widely observed in mass spectrometry (MS)-based omics studies and could be recognized as missing not at random (MNAR). MNAR leads to biased statistical estimations and jeopardizes downstream analyses. Although a wide range of missing value imputation methods was developed for omics studies, a limited number of methods were designed appropriately for the situation of MNAR. To facilitate MS-based omics studies, we introduce GSimp, a Gibbs sampler-based missing value imputation approach, to deal with left-censor missing values in MS-proteomics datasets. In this book, we explain the MNAR and elucidate the usage of GSimp for MNAR in detail.

Keywords: Gibbs sampler; Imputation; Left censor; Mass spectrometry; Missing not at random; Proteomics.

MeSH terms

Algorithms*
Data Collection
Limit of Detection
Mass Spectrometry / methods
Proteomics*