ArticlePLoS computational biology2018
GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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75 citing papers in PubMed.
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- Multi-center multi-omics integration predicts individualized prognosis in medullary thyroid carcinoma.Nature communications · 2026Article
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- Metabolic features in plasma and urine of obese children and their association with MAFLD risk.Frontiers in nutrition · 2026Article
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- Beyond Bulk Metabolomics: Emerging Technologies for Defining Cell-Type Specific Metabolic Pathways in Health and Disease.Biomolecules · 2025Review
- Benchmarking porcine pancreatic ductal organoids for drug screening applications.EMBO molecular medicine · 2025Article
- Identification of protein biomarkers to differentiate between gram-negative and gram-positive infections in adults suspected of sepsis.BMC infectious diseases · 2025Article
- Impact of climate change driven freshening, warming, and ocean acidification on the cellular metabolism of Atlantic Cod (Gadus morhua).Scientific reports · 2025Article
- Reversible compromise of physiological resilience by accumulation of heteroplasmic mtDNA mutations.Science (New York, N.Y.) · 2025Article
- The polyunsaturated fatty acid and oxylipin plasma signature of aneurysmal subarachnoid haemorrhage, case-control study.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Article
- Serum and nasal lavage fluid eosinophil-derived neurotoxin levels and their determinants in adults.The journal of allergy and clinical immunology. Global · 2025Article
- Insight into the metabolic shifts in Graves' hyperthyroidism: a study of acylcarnitine and lipid profiles.European thyroid journal · 2025Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.