ArticleNucleic acids research2020
NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed.
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- Three phosphatidylcholines as a potential early-diagnostic signature for hyperglycemia in preschool-aged children: a cross-sectional pilot study.Frontiers in endocrinology · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- LimROTS: a hybrid method integrating empirical Bayes and reproducibility-optimized statistics for robust differential expression analysis.Bioinformatics (Oxford, England) · 2025Article
- Dual-Omics Mapping of Tinnitus Phenotype Transition in Noise-Exposed Auditory Cortex.Cellular and molecular neurobiology · 2025Article
- Phosphoproteomic profiling of lipopolysaccharide stimulated toll-like receptor pathways in macrophages.Scientific data · 2025Article
- Benchmarking informatics workflows for data-independent acquisition single-cell proteomics.Nature communications · 2025Article
- Signatures of proteomics and glycoproteomics revealed liraglutide ameliorates MASLD by regulating specific metabolic homeostasis in mice.Journal of pharmaceutical analysis · 2025Article
- Mutation-induced filaments of folded proteins are inert and non-toxic in a cellular system.Molecular systems biology · 2025Article
- Landscape and mCell genomics · 2025Article
- Cerebrospinal fluid quantitative proteomic reveals potential mechanisms and biomarker candidates of children with bacterial meningitis complicated by neurological complications.Clinical proteomics · 2025Article
20 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.
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