ArticleScientific reports2020
Differential Abundance Analysis with Bayes Shrinkage Estimation of Variance (DASEV) for Zero-Inflated Proteomic and Metabolomic Data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A nutritionally informed model for Bayesian variable selection with metabolite response variables.Biostatistics (Oxford, England) · 2026Article
- A Bayesian Informative Shrinkage Approach for Large-scale Multiple Hypothesis Testing (BISHOT): with Applications in Differential Analysis of Omics Data.bioRxiv : the preprint server for biology · 2025Article
- Designing a Comparative Proteomics Experiment: Retention-Time Alignment and Imputation Algorithms Affect Statistical Comparisons between Samples.Journal of proteome research · 2025Article
- Single-Cell Peptide Profiling to Distinguish Stickleback Ecotypes with Divergent Breeding Behavior.Journal of proteome research · 2025Article
- Evaluation of imputation and imputation-free strategies for differential abundance analysis in metaproteomics data.Briefings in bioinformatics · 2025Article
- Reduced circulating sphingolipids andScience advances · 2025Article
- A Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data.Metabolites · 2022Review
- Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data.Briefings in bioinformatics · 2022Article
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7 authors.
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Abstract
Mass spectrometry (MS) is frequently used for proteomic and metabolomic profiling of biological samples. Data obtained by MS are often zero-inflated. Those zero values are called point mass values (PMVs). Zero values can be further grouped into biological PMVs and technical PMVs. The former type is caused by true absence of a compound and the later type is caused by a technical detection limit. Methods based on a mixture model have been developed to separate the two types of zeros and to perform differential abundance analysis comparing proteomic/metabolomic profiles between different groups of subjects. However, we notice that those methods may give unstable estimate of the model variance, and thus lead to false positive and false negative results when the number of non-zero values is small. In this paper, we propose a new differential abundance analysis method, DASEV, which uses an empirical Bayes shrinkage method to more robustly estimate the variance and enhance the accuracy of differential abundance analysis. Simulation studies and real data analysis show that DASEV substantially improves parameter estimation of the mixture model and outperforms current methods in identifying differentially abundant features.
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