CAMPP

CAMPP performs standardized statistical and network analyses of quantitative -omics and next-generation sequencing (NGS) data to identify cancer biomarkers and assess prognostic factors.

Key Features:

• R-based implementation: CAMPP functions as an R-based wrapper that orchestrates the included analytical methods.
• Data Management: Implements missing value imputation, normalization procedures, and distributional checks to ensure data quality.
• Clustering: Performs k-means clustering to identify sample or feature groupings.
• Differential Analysis: Conducts differential expression or abundance analysis to detect significant changes in biological markers.
• Regression Analysis: Uses elastic-net regression for predictive modeling and feature selection.
• Network Analyses: Supports correlation and co-expression network analyses as well as protein–protein and miRNA–gene interaction networks.
• Survival Analysis: Includes survival analysis to evaluate prognostic markers in cancer studies.
• Reproducibility: Employs an renv .lock file to lock R package versions for computational reproducibility.
• Output Formats: Generates tabular files and graphical representations of results.

Scientific Applications:

• Cancer biomarker discovery: Identifies candidate biomarkers from -omics and NGS datasets through differential and regression analyses.
• Molecular mechanism exploration: Analyzes molecular interactions and co-expression to investigate protein–protein and miRNA–gene relationships.
• Prognostic factor assessment: Evaluates prognostic markers in cancer cohorts using survival analysis.
• Standardized comparative analyses: Provides standardized analyses to facilitate comparability across high-throughput studies.

Methodology:

Includes missing value imputation, normalization and distributional checks, k-means clustering, differential expression/abundance analysis, elastic-net regression, correlation and co-expression network analyses, protein–protein and miRNA–gene interaction network analysis, survival analysis, and use of an R-based wrapper with an renv .lock file to lock R-package versions.