ArticleStatistical applications in genetics and molecular biology2013
Accounting for undetected compounds in statistical analyses of mass spectrometry 'omic studies.
Article in Statistical applications in genetics and molecular biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A priori sample size determination and power analysis in metabolic phenotyping and integrative metabolomics: an application framework based on a systematic review of literature.Metabolomics : Official journal of the Metabolomic Society · 2026Pooled it
- Bayesian Sparse Regression for Microbiome-Metabolite Data Integration.Statistics in medicine · 2026Article
- A nutritionally informed model for Bayesian variable selection with metabolite response variables.Biostatistics (Oxford, England) · 2026Article
- Evaluation of imputation and imputation-free strategies for differential abundance analysis in metaproteomics data.Briefings in bioinformatics · 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
- Article
- Metabolomics reveals biomarkers in human urine and plasma to predict cytochrome P450 2D6 (CYP2D6) activity.British journal of pharmacology · 2021Article
- Estimating drug consumption during a college sporting event from wastewater using liquid chromatography mass spectrometry.The Science of the total environment · 2021Article
- PolySTest: Robust Statistical Testing of Proteomics Data with Missing Values Improves Detection of Biologically Relevant Features.Molecular & cellular proteomics : MCP · 2020Article
- Differential Abundance Analysis with Bayes Shrinkage Estimation of Variance (DASEV) for Zero-Inflated Proteomic and Metabolomic Data.Scientific reports · 2020Article
- High-resolution metabolomic profiling of Alzheimer's disease in plasma.Annals of clinical and translational neurology · 2020Article
- BayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach.BMC bioinformatics · 2019Article
- Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.Scientific reports · 2018Article
- Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices.Briefings in bioinformatics · 2017Article
- Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies.BMC bioinformatics · 2017Article
- Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.Bioinformatics (Oxford, England) · 2017Article
- Integrated analysis of proteome and transcriptome changes in the mucopolysaccharidosis type VII mouse hippocampus.Molecular genetics and metabolism · 2016Article
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3 authors.
Funding
Abstract
Mass spectrometry is an important high-throughput technique for profiling small molecular compounds in biological samples and is widely used to identify potential diagnostic and prognostic compounds associated with disease. Commonly, this data generated by mass spectrometry has many missing values resulting when a compound is absent from a sample or is present but at a concentration below the detection limit. Several strategies are available for statistically analyzing data with missing values. The accelerated failure time (AFT) model assumes all missing values result from censoring below a detection limit. Under a mixture model, missing values can result from a combination of censoring and the absence of a compound. We compare power and estimation of a mixture model to an AFT model. Based on simulated data, we found the AFT model to have greater power to detect differences in means and point mass proportions between groups. However, the AFT model yielded biased estimates with the bias increasing as the proportion of observations in the point mass increased while estimates were unbiased with the mixture model except if all missing observations came from censoring. These findings suggest using the AFT model for hypothesis testing and mixture model for estimation. We demonstrated this approach through application to glycomics data of serum samples from women with ovarian cancer and matched controls.
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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.