QPROT

QPROT performs differential protein expression analysis on protein intensity data to identify proteins with significant changes across conditions in label-free quantitative proteomics.

Key Features:

• QSPEC extension: QPROT extends the QSPEC framework from spectral count data to continuously measured protein-level intensity data.
• Intensity normalization and model-based analysis: Implements a novel intensity normalization procedure and model-based differential expression analysis for protein intensity data.
• Statistical framework: Uses a standardized Z-statistic derived from the posterior distribution of the log fold change parameter and employs an Empirical Bayes method to estimate false discovery rate (FDR).
• Handling missing data: Applies a probabilistic treatment for missing values in protein intensity data.
• Evaluation and performance: Performance assessed using Clinical Proteomic Technology Assessment for Cancer (CPTAC) quantification calibration data and an Escherichia coli benchmark dataset, demonstrating accuracy in FDR control.

Scientific Applications:

• Comparative quantitative proteomics: Identification of differentially expressed proteins across experimental conditions using label-free protein intensity data.
• Label-free quantitative proteomics workflows: Processing and statistical analysis of continuously measured protein-level intensity data.
• Large-scale proteomic studies: Application to studies with missing values and complex experimental designs, including clinical proteomics such as CPTAC datasets.

Methodology:

Extends QSPEC methodology to protein intensity data, applies a novel intensity normalization procedure, performs model-based differential expression analysis with probabilistic handling of missing values, and computes standardized Z-statistics from the posterior distribution of log fold change with Empirical Bayes estimation of FDR.