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Code for Ash et al, Structure Based Approach to Exploring and Modeling Trait-Associated Metabolite Profiles

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Cheminformatics Based Approach to Exploring and Modeling Trait-Associated Metabolite Profiles

Jeremy R. Ash, Melaine A. Kuenemann, Daniel Rotroff, Alison Motsinger-Reif, and Denis Fourches

Usage

To run any of the r scripts provided, clone the repository and open the metabochem.Rproj file in Rstudio. Run the scripts within the project, so that they can find the relevant paths on your machine.

Files

  • The analyses folder contains three rmarkdown files and one r script, which perform the majority of the analyses reported in the paper.
    • differential_analyses.rmd: Performs the differential analysis on cancer and healthy patient metabolites. Outputs the significance results. See already rendered report: differential_analysis.pdf.
    • metab_clus.r: Filters metabolites by significance and clusters on chemical structure. Outputs clustered metabolite concentration profiles.
    • metab_classifier_plasma.rmd: Takes the clustered plasma metabolite concentration profiles as input. Builds and validates classification models predicting patient cancer status. See already rendered report: metab_classifier_plasma.html.
    • metab_classifier_serum.rmd: Takes the clustered serum metabolite concentration profiles as input. Builds and validates classification models predicting patient cancer status. See already rendered report: metab_classifier_serum.html.
  • The analyses/data folder contains the data used by each analysis script.
    • sample_metabolites_training_excol_fix.csv, sample_metabolites_test.txt: raw patient metabolite concentration profiles.
    • training_set_tq_normalize.rda, test_set_tq_normalize.rda: fully processed and normalized patient metabolite concentration profiles.
    • sample_factors_train.txt, ssample_factors_test.txt: patient characteristic data, including cancer status.
    • common_test_training_molecule-v2.sdf: metabolite chemical structures.
    • metabolics_fingerprint.csv: metabolite fingerprints.
    • ML_data.RDATA: clustered metabolites concentration profiles.
  • The analyses/data folder/signficance_results folder contains the signficance_results from the differential analysis.
  • The analyses/figure_scripts folder contains the scripts to recreate a few of the figures in the paper. The file names contain the corresponding figure numbers.

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Code for Ash et al, Structure Based Approach to Exploring and Modeling Trait-Associated Metabolite Profiles

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