Introduction and Objective: Cytokine profiling is crucial in understanding immune responses and disease mechanisms, yet existing analytical tools often fall short in addressing the unique complexities of cytokine data. This study introduces CytoProfile, an R-based software tailored for comprehensive cytokine analysis. The objective was to evaluate its integration of advanced statistical and machine learning methods for precise classification and biomarker identification in immunological research.Methods: CytoProfile integrates data preprocessing and visualization tools, alongside advanced statistical analyses and machine learning methods. Cytokine data derived from peripheral blood mononuclear cell (PBMCs) under varied stimulations were analyzed using its comprehensive pipeline. Comparative analyses were performed against tools like CytokineExplore, MetaboAnalyst and Qlucore Omics Explorer, focusing on visualization, classification accuracy, and feature importance metrics.Results: CytoProfile demonstrated superior functionality, including dual-flashlight plots, enriched error bar visualizations, and effect-size-based assessments. Random Forest and XGBoost algorithms provided robust classification, achieving an average AUC of >0.80. Key cytokines linked to disease states were effectively identified, with CytoProfile outperforming alternatives in interpretability and analytical depth. Comparative evaluations highlighted its user-friendly interface and versatility in handling high-dimensional cytokine data.Conclusion: CytoProfile addresses critical gaps in cytokine data analysis by integrating statistical precision with machine learning and visualization techniques. It allows researchers to uncover biologically significant insights, advancing cytokine research and its diagnostic and therapeutic applications. Future updates aim to incorporate support vector machine and neural networks to further enhance analytical capabilities.
S. Saraswat: None. P.A. Kern: None. G. Kalantar: None. B. Nikolajczyk: None. X.D. Zhang: None.
US National Institutes of Health (U01DK135111, R01AG084180, R01AG079525 and UL1TR001998); Diabetes Research Center, Washington University (P30DK020579)
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