A new workflow combining R packages for statistical analysis of metabolites
Ferrario, P. G.
Show abstract
In metabolomics, the investigation of an association between many metabolites and one trait (such as age in humans or cultivar in foods) is a central research question. On this topic, we present a complete statistical analysis, combining selected R packages in a new workflow, which we are sharing completely, according to modern standards and research reproducibility requirements. We demonstrate the workflow using a large-scale study with public data, available on repositories. Hence, the workflow can directly be re-used on quite different metabolomics data, when searching for association with one covariate of interest.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SGI: Automatic clinical subgroup identification in omics datasets 95%
- metGWAS 1.0: An R workflow for network-driven over-representation analysis between independent metabolomic and meta-genome wide association studies 93%
- Pathway Volcano: An interactive tool for pathway guided visualization of differential expression data 93%
Similar papers in this journal
- Probabilistic quotient's work and pharmacokinetics' contribution: countering size effect in metabolic time series measurements 95%
- Single sample pathway analysis in metabolomics: performance evaluation and application 94%
- GraphOmics: an Interactive Platform to Explore and Integrate Multi-Omics Data 93%
Similar papers in this journal
- A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis 92%
- Blood-based transcriptomic signature panel identification for cancer diagnosis: Benchmarking of feature extraction methods 92%
- Molecular Group and Correlation Guided Structural Learning for Multi-Phenotype Prediction 92%
Similar papers in this journal
- Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline 94%
- Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis 94%
- Genome scale metabolic network modelling for metabolic profile predictions 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.