Nevin Manimala Statistics

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.


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.

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

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