IRIS dataset

This work seeks to develop a methodology for identifying reliable biomarkers of disease activity, progression and outcome through the identification of significant associations between high-throughput flow cytometry data and interstitial lung disease in systemic sclerosis patients.
Conclusion:
The combination of Conditional Random Forests and Gene Set Enrichment Analysis was successful in identifying a subset of flow cytometry variables to create a screening tool that proved effective in correctly identifying ILD patients in the training and validation data sets. Cytometer setup using CST in FACSDiva software.