https://github.com/barbarahelena/ckd-metabolomics
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (6.9%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: barbarahelena
- Language: R
- Default Branch: main
- Size: 1.62 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
HELIUS: Metabolome profiles in chronic kidney disease
Author: Barbara Verhaar, b.j.verhaar@amsterdamumc.nl
Summary
File structure
The scripts in this repo include the following:
Descriptives
CKD subjects and controls were selected from the HELIUS cohort based on the following criteria:
- ACR-KDIGO albuminuria stage A1 (controls) or A2 (CKD)
- Availability of plasma, urine and fecal samples
Machine learning analyses
For the machine learning analyses, a XGBoost algorithm was used in a nested cross-validated design with 100 iterations. The script for these analyses can be found in the XGBoost.py file. For each model, the study population was divided in a training (80%) and test (20%) set. For each iteration, the model was trained on the training set on which the hyperparameters were optimized in a 5-fold CV. The resulting model was tested on the test set. The parameter grid can be found in param_grid_medium.json in the scripts/xgboost folder. Each model resulted in a ROC and feature importance of predictors, averaged over 200 iterations.
In the process_model_results_class.R file, the functions from the functions.R file are used to generate feature importance plots, and violin plots for best predictors to assess differences in concentrations between groups.
The following models were used:
- Plasma metabolite profiles non-diabetic CKD versus healthy controls: AUC 0.59
- Urine metabolite profiles non-diabetic CKD versus healthy controls: AUC 0.65
Plasma metabolites
{width=400px}
Urine metabolites
{width=400px}
Regression analyses with highest ranked predictors
Overlap plasma and urine metabolites
Owner
- Name: Barbara Verhaar
- Login: barbarahelena
- Kind: user
- Location: Amsterdam
- Twitter: BarbaraVerhaar
- Repositories: 1
- Profile: https://github.com/barbarahelena
PhD candidate @ Amsterdam UMC Vascular medicine
GitHub Events
Total
- Push event: 1
Last Year
- Push event: 1