prot2D

prot2D simulates normalization, statistical testing, and false discovery rate control to improve accuracy in proteomic data analysis.

Key Features:

• Normalization: Addresses high dimensionality in 2-DE data to enable accurate comparisons between protein expression levels.
• Statistical Testing: Implements the moderate t-test to improve sensitivity and reliability of detecting significant changes in protein expression.
• False Discovery Rate (FDR) Control: Applies Benjamini and Hochberg methods to minimize false positives in large-scale proteomics studies.

Scientific Applications:

• Biomarker discovery in human proteomics: Distinguishes protein expression patterns between healthy and diseased states.
• Comparative animal proteomics: Compares protein responses between stressed and control conditions in animal studies.

Methodology:

Simulates normalization, statistical testing (including the moderate t-test) and false discovery rate control using Benjamini and Hochberg methods, and evaluates these procedures on real and simulated datasets.