prolfqua

prolfqua performs integrated statistical analysis of mass spectrometry-based proteomics data for relative protein quantification and differential expression analysis.

Key Features:

• Integration of workflow steps: Implements quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation within a single workflow.
• Modular design: Supports incorporation of new data formats and adapts to simple designs with a single explanatory variable as well as complex experiments with multiple factors and contrasts.
• Statistical modeling and hypothesis testing: Provides methods for sensitive and specific differential expression analysis and for modeling a variety of experimental designs.
• Benchmarking functionality: Enables benchmarking of data acquisition, preprocessing, and modeling methods against a gold standard dataset.

Scientific Applications:

• Quantitative Proteomics Studies: Supports relative protein quantification and differential expression analysis in mass spectrometry-based proteomics experiments.
• Experimental Design Analysis: Facilitates modeling and hypothesis testing for multifactorial experimental designs.
• Data Integration and Comparison: Allows integration of diverse proteomics data formats and comparison of analytical methods through benchmarking against gold standard datasets.

Methodology:

Quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation.