ReviewMetabolites2022
A Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data.
Review in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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Who cites it
7 citing papers in PubMed.
- Pathway metabolite ratios reveal distinctive glutamine metabolism in a subset of proliferating cells.Molecular systems biology · 2025Article
- Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields.Genes · 2025Review
- Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics.Briefings in bioinformatics · 2025Review
- Precision nutrition: Is tailor‑made dietary intervention a reality yet? (Review).Biomedical reports · 2025Review
- Prediction Modeling With Many Correlated and Zero-Inflated Predictors: Assessing the Nonnegative Garrote Approach.Statistics in medicine · 2025Article
- Physiological and evolutionary contexts of a new symbiotic species from the nitrogen-recycling gut community of turtle ants.The ISME journal · 2023Article
- Deriving Convergent and Divergent Metabolomic Correlates of Pulmonary Arterial Hypertension.Metabolites · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This review presents an overview of the statistical methods on differential abundance (DA) analysis for mass spectrometry (MS)-based metabolomic data. MS has been widely used for metabolomic abundance profiling in biological samples. The high-throughput data produced by MS often contain a large fraction of zero values caused by the absence of certain metabolites and the technical detection limits of MS. Various statistical methods have been developed to characterize the zero-inflated metabolomic data and perform DA analysis, ranging from simple tests to more complex models including parametric, semi-parametric, and non-parametric approaches. In this article, we discuss and compare DA analysis methods regarding their assumptions and statistical modeling techniques.
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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.