ReviewJournal of proteome research2021
A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics.
Review in Journal of proteome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments.Nature methods · 2023Guideline
- Aggregation Methods for Quantifying PTM and Structural Changes in Bottom-Up Proteomics.Journal of proteome research · 2026Article
- LIN28A-Dependent Kinome and Phosphoproteome Reprogramming Promotes Imatinib Resistance.Molecular & cellular proteomics : MCP · 2026Article
- Placental proteomic signatures of preterm birth, gestational age, and birthweight.BMC pregnancy and childbirth · 2025Article
- Proteomics Data Imputation With a Deep Model That Learns From Many Datasets.Molecular & cellular proteomics : MCP · 2025Article
- Identification and applications of disease-associated differential human and bacterial proteins with metaproteomic evidence.Health information science and systems · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.The annals of applied statistics · 2025Article
- Improved quantitative accuracy in data-independent acquisition proteomics via retention time boundary imputation.bioRxiv : the preprint server for biology · 2025Article
- Assessing the impact of batch effect associated missing values on downstream analysis in high-throughput biomedical data.Briefings in bioinformatics · 2025Article
- Evaluation of imputation and imputation-free strategies for differential abundance analysis in metaproteomics data.Briefings in bioinformatics · 2025Article
- Cross-platform proteomics signatures of extreme old age.GeroScience · 2025Article
- Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes.Scientific data · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- RiceProteomeDB (RPDB): a user-friendly database for proteomics data storage, retrieval, and analysis.Scientific reports · 2024Article
- Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors.Scientific reports · 2023Article
- Evaluating Proteomics Imputation Methods with Improved Criteria.Journal of proteome research · 2023Article
- Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization.BMC bioinformatics · 2023Article
- Plasma protein levels of young healthy pigs as indicators of disease resilience.Journal of animal science · 2023Article
- Missing data in multi-omics integration: Recent advances through artificial intelligence.Frontiers in artificial intelligence · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is different than unlabeled studies, which prompts the need for this review to compare the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets to guide researchers on the appropriateness of various imputation methods. Imputation methods were evaluated by accuracy, statistical hypothesis test inference, and run time. In general, expectation maximization and random forest imputation methods yielded the best performance, and constant-based methods consistently performed poorly across all data set sizes and percentages of missing values. For data sets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. On the basis of the findings in this review, there are core imputation methods that perform better for isobaric-labeled proteomics data, but great care and consideration as to whether imputation is the optimal strategy should be given for data sets comprised of a small number of samples.
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Registered trials
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.