PhosR

PhosR provides comprehensive processing and functional analysis of mass spectrometry (MS)-based phosphoproteomic data to profile phosphorylation-driven signaling in cells and tissues.

Key Features:

• Data processing: Includes filtering, imputation, normalization, batch correction, and integration of multiple phosphoproteomic datasets.
• Imputation using 'stably phosphorylated sites': Implements imputation and normalization strategies that leverage 'stably phosphorylated sites' for improved data accuracy.
• Site- and protein-centric analysis: Performs both site- and protein-centric pathway analysis to evaluate kinase activities and signaling pathways.
• Kinase–substrate annotation: Supports large-scale kinase–substrate annotation derived from dynamic phosphoproteomic profiling.
• Signalome construction: Provides a method to construct 'signalomes' that summarize and visualize signaling modules and kinase interactions.

Scientific Applications:

• Kinase activity inference: Infers the action of kinases from phosphoproteomic experiments.
• Pathway dynamics analysis: Analyzes signaling pathway dynamics from temporal or condition-specific phosphoproteomic data.
• Cross-study integration: Integrates published and novel phosphoproteomic datasets to derive comparative insights.
• Robust data preprocessing: Applies imputation and normalization using 'stably phosphorylated sites' to enhance analytical precision.

Methodology:

Implemented as a suite of R packages, PhosR performs filtering, imputation using 'stably phosphorylated sites', normalization, batch correction, kinase–substrate annotation from dynamic profiling, site- and protein-centric pathway analysis, and construction of 'signalomes'.