ArticleAnalytical chemistry2017
Joint Bounding of Peaks Across Samples Improves Differential Analysis in Mass Spectrometry-Based Metabolomics.
Article in Analytical chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Flimma: a federated and privacy-aware tool for differential gene expression analysis.Genome biology · 2021Pooled it
- AI redefine untargeted metabolomics: estimating chemical amounts for a Human Exposome Project.Frontiers in public health · 2026Review
- The data substrate of exposome intelligence: an interoperability profile for untargeted metabolomics.Frontiers in artificial intelligence · 2026Article
- Privacy-preserving multicenter differential protein abundance analysis with FedProt.Nature computational science · 2025Article
- AI redefines mass spectrometry chemicals identification: retention time prediction in metabolomics and for a Human Exposome Project.Frontiers in public health · 2025Review
- Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases.The Journal of clinical investigation · 2020Review
- Article
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
- Metabolite Profiles of Healthy Aging Index Are Associated With Cardiovascular Disease in African Americans: The Health, Aging, and Body Composition Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2019Article
- From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.GigaScience · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
As mass spectrometry-based metabolomics becomes more widely used in biomedical research, it is important to revisit existing data analysis paradigms. Existing data preprocessing efforts have largely focused on methods which start by extracting features separately from each sample, followed by a subsequent attempt to group features across samples to facilitate comparisons. We show that this preprocessing approach leads to unnecessary variability in peak quantifications that adversely impacts downstream analysis. We present a new method, bakedpi, for the preprocessing of both centroid and profile mode metabolomics data that relies on an intensity-weighted bivariate kernel density estimation on a pooling of all samples to detect peaks. This new method reduces this unnecessary quantification variability and increases power in downstream differential analysis.
Indexed as
Identifiers
What OpenQuestion holds
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.