ArticleThe annals of applied statistics2018
The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments.
Article in The annals of applied statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Protein Variability Patterns in Ovarian Serous Carcinoma.International journal of molecular sciences · 2026Article
- A Bayesian framework for multivariate differential analysis.PLoS computational biology · 2026Article
- Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation.Journal of proteome research · 2024Article
- Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience.Proteomes · 2024Article
- A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.Nature methods · 2024Article
- Standardized Workflow for Mass-Spectrometry-Based Single-Cell Proteomics Data Processing and Analysis Using the scp Package.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Neither random nor censored: estimating intensity-dependent probabilities for missing values in label-free proteomics.Bioinformatics (Oxford, England) · 2023Article
- Challenges and Opportunities for Single-cell Computational Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Conditional Fragment Ion Probabilities Improve Database Searching for Nonmonoisotopic Precursors.Journal of proteome research · 2023Article
- Separation methods in single-cell proteomics: RPLC or CE?International journal of mass spectrometry · 2022Article
- Article
- Prior Signal Acquisition Software Versions for Orbitrap Underestimate Low Isobaric Mass Tag Intensities, Without Detriment to Differential Abundance Experiments.ACS measurement science au · 2022Article
- Challenges and Opportunities for Bayesian Statistics in Proteomics.Journal of proteome research · 2022Review
- Quantitative Accuracy and Precision in Multiplexed Single-Cell Proteomics.Analytical chemistry · 2022Review
- Article
- Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics.International journal of molecular sciences · 2021Article
- IceR improves proteome coverage and data completeness in global and single-cell proteomics.Nature communications · 2021Article
- The rise of single-cell proteomics.Analytical science advances · 2021Review
- Subcellular proteomics.Nature reviews. Methods primers · 2021Article
- Article
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7 authors.
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Abstract
An idealized version of a label-free discovery mass spectrometry proteomics experiment would provide absolute abundance measurements for a whole proteome, across varying conditions. Unfortunately, this ideal is not realized. Measurements are made on peptides requiring an inferential step to obtain protein level estimates. The inference is complicated by experimental factors that necessitate relative abundance estimation and result in widespread non-ignorable missing data. Relative abundance on the log scale takes the form of parameter contrasts. In a complete-case analysis, contrast estimates may be biased by missing data and a substantial amount of useful information will often go unused. To avoid problems with missing data, many analysts have turned to single imputation solutions. Unfortunately, these methods often create further difficulties by hiding inestimable contrasts, preventing the recovery of interblock information and failing to account for imputation uncertainty. To mitigate many of the problems caused by missing values, we propose the use of a Bayesian selection model. Our model is tested on simulated data, real data with simulated missing values, and on a ground truth dilution experiment where all of the true relative changes are known. The analysis suggests that our model, compared with various imputation strategies and complete-case analyses, can increase accuracy and provide substantial improvements to interval coverage.
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