ArticleBMC bioinformatics2015
Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- A New Perspective on Gas Chromatography-Mass Spectrometry Urinary Metabolomic Analysis and Efficient Risk Assessment of Urolithiasis: Morning Urine Organic Acid Profiles.Kidney & blood pressure research · 2025Article
- Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics.PLoS computational biology · 2024Article
- Compositional Data Analysis using Kernels in mass cytometry data.Bioinformatics advances · 2022Article
- PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.PLoS computational biology · 2021Article
- Variant-set association test for generalized linear mixed model.Genetic epidemiology · 2021Article
- Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra.Bioinformatics (Oxford, England) · 2020Article
- Predictive Modeling for Metabolomics Data.Methods in molecular biology (Clifton, N.J.) · 2020Article
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
- Article
- Robust volcano plot: identification of differential metabolites in the presence of outliers.BMC bioinformatics · 2018Article
- Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.Scientific reports · 2018Article
- A fast small-sample kernel independence test for microbiome community-level association analysis.Biometrics · 2017Article
- Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.Genetics · 2017Article
- From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.GigaScience · 2017Review
- Joint Bounding of Peaks Across Samples Improves Differential Analysis in Mass Spectrometry-Based Metabolomics.Analytical chemistry · 2017Article
- Metabolomics variable selection and classification in the presence of observations below the detection limit using an extension of ERp.BMC bioinformatics · 2017Article
- Navigating freely-available software tools for metabolomics analysis.Metabolomics : Official journal of the Metabolomic Society · 2017Review
- A novel copy number variants kernel association test with application to autism spectrum disorders studies.Bioinformatics (Oxford, England) · 2016Article
- Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment.Analytical chemistry · 2016Article
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3 authors.
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Abstract
backgroundData generated from metabolomics experiments are different from other types of "-omics" data. For example, a common phenomenon in mass spectrometry (MS)-based metabolomics data is that the data matrix frequently contains missing values, which complicates some quantitative analyses. One way to tackle this problem is to treat them as absent. Hence there are two types of information that are available in metabolomics data: presence/absence of a metabolite and a quantitative value of the abundance level of a metabolite if it is present. Combining these two layers of information poses challenges to the application of traditional statistical approaches in differential expression analysis.
resultsIn this article, we propose a novel kernel-based score test for the metabolomics differential expression analysis. In order to simultaneously capture both the continuous pattern and discrete pattern in metabolomics data, two new kinds of kernels are designed. One is the distance-based kernel and the other is the stratified kernel. While we initially describe the procedures in the case of single-metabolite analysis, we extend the methods to handle metabolite sets as well.
conclusionsEvaluation based on both simulated data and real data from a liver cancer metabolomics study indicates that our kernel method has a better performance than some existing alternatives. An implementation of the proposed kernel method in the R statistical computing environment is available at http://works.bepress.com/debashis_ghosh/60/ .
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