PRONE

PRONE enables comparative evaluation of normalization methods for proteomics data, including their impact on downstream analyses such as differential expression analysis and statistical enrichment analysis.

Key Features:

• Preprocessing functions: The package provides functions for preprocessing proteomics data prior to normalization.
• Normalization methods: The package implements multiple normalization approaches for comparative evaluation.
• Normalization evaluation: The package evaluates normalization methods using unnormalized and normalized data comparisons.
• Downstream analysis evaluation: Normalization methods can be evaluated on downstream steps including differential expression analysis and statistical enrichment analysis.
• Support for multiple data types: Spike-in datasets with known ground truth and real-world datasets acquired by tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

Scientific Applications:

• Normalization strategy selection: Identifying appropriate normalization strategies for specific proteomics datasets through comparative evaluation.
• Systematic bias correction assessment: Evaluating how effectively normalization methods adjust for systematic biases introduced during sample collection through quantification in clinical studies.
• Downstream analysis impact assessment: Determining how normalization substantially impacts downstream analyses in a dataset-specific manner.
• Spike-in validation: Validating normalization methods using spike-in datasets with known ground truth.

Methodology:

PRONE was developed as a unifying R package for comparative evaluation of normalization methods. The systematic evaluation in the original study assessed 17 normalization and 2 batch effect correction methods on 6 publicly available spike-in and 3 label-free and tandem mass tag datasets.