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.
CITATION STYLE
Wei, R., & Wang, J. (2023). Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with GSimp. In Methods in Molecular Biology (Vol. 2426, pp. 119–129). Humana Press Inc. https://doi.org/10.1007/978-1-0716-1967-4_6
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