ArticleMolecules (Basel, Switzerland)2024
Application of Weighted Gene Co-Expression Network Analysis to Metabolomic Data from an ApoA-I Knockout Mouse Model.
Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Article
- Anti-psychotic therapeutics modify gut microbial metabolism and modulate susceptibility to gastrointestinal infection in mice.bioRxiv : the preprint server for biology · 2026Article
- Protein co-expression network analysis explores biological mechanisms associated with meat quality variability in broilers affected by breast myopathies: A focus on wooden breast.Poultry science · 2026Article
- Eigenlipids for exploring lipid biology.Journal of lipid research · 2026Review
- Metabolomic responses are more sensitive in muscle than serum following 28 days of arduous exercise with erythropoietin administration.Experimental physiology · 2026Article
- Liver and Skeletal Muscle Metabolome Characterization in Peripartal Dairy Cows Fed Rumen-Protected Methionine or Rumen-Protected Choline.Animals : an open access journal from MDPI · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography-Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to reduce the dimension of a dataset, the impact of phenotypic traits such as diet type and genotype on the plasma metabolome was evaluated quantitatively, and hub metabolites were found based on the network graph. Our work revealed that WGCNA is a very powerful tool to decipher, interpret, and visualize metabolomic datasets.
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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.