XPlatCom (Cross PLATform COMmander)

XPlatCom (Cross PLATform COMmander) integrates quantitative Proteomics and Transcriptomics data and performs data conversion, quality control, comparative and differential statistical analyses including Gene Ontology (GO) and pathway analyses to support biomarker identification in multi-OMICS research.

Key Features:

• Data Integration: Integrates quantitative Proteomics and Transcriptomics datasets for joint analysis.
• Data Conversion: Performs data conversion for multi-OMICS inputs.
• Quality Control: Executes quality control procedures on input datasets.
• Comparative and Differential Statistical Analyses: Conducts comparative analyses and differential/statistical evaluations across datasets.
• Gene Ontology (GO) and Pathway Analyses: Performs Gene Ontology (GO) and pathway analyses for functional interpretation.
• Semi-automatic Workflow: Provides a semi-automatic workflow for multi-OMICS data processing.
• Biomarker Identification and Annotation: Detects, ranks, and annotates candidate biomarkers using existing biological knowledge.
• Linear Regression Analyses: Performs linear regression analyses between Transcriptomics and Proteomics and notes limitations of simple linear models for complex relationships.
• Planned Multivariate and Classification Methods: Plans to incorporate multivariate variable selection and classification methods.

Scientific Applications:

• PROFILE project — liver disease biomarker discovery: Supports identification of novel biomarkers and therapeutic targets for liver diseases such as hepatocellular carcinoma using integrated patient Transcriptomics and Proteomics data.
• Functional interpretation of multi-OMICS data: Enables GO and pathway analyses to interpret molecular changes across Proteomics and Transcriptomics layers.

Methodology:

Uses a structured, semi-automatic workflow that includes data conversion, quality control, comparative analyses, differential/statistical evaluations, integration of Proteomics and Transcriptomics, Gene Ontology (GO) and pathway analyses, and linear regression between Transcriptomics and Proteomics.