proteiNorm

proteiNorm provides normalization assessment and analysis for mass spectrometry-based proteomics data to correct systemic biases and support downstream differential expression and statistical power estimation.

Key Features:

• Data Input Compatibility: Accepts tab-separated peptide (optional) and protein data files not on a logarithmic scale, including outputs from MaxQuant with intensity columns labeled like “Reporter intensity corrected” followed by an integer and optional label (e.g., “Reporter intensity corrected 5 TMT2”), to support tandem mass tag (TMT) experiments.
• Normalization Method Evaluation: Applies preliminary filters at peptide and sample levels and enables application and comparison of multiple normalization methods to identify suitable approaches for specific experimental designs.
• Visualization and Imputation: Visualizes missing values and implements various imputation methods to support selection of normalization strategies and improve completeness for downstream analyses.
• Differential Expression Analysis: Compares different differential expression methods and estimates statistical power following normalization.

Scientific Applications:

• TMT multiplex experiments: Evaluation and comparison of normalization strategies for TMT-based multiplexing such as TMT6plex and TMT10plex experiments.
• Label-free spike-in mass spectrometry datasets: Assessment of normalization effects and downstream differential expression analysis in label-free spike-in experimental designs.
• Normalization impact assessment: Investigation of how different normalization methods affect data interpretation and statistical conclusions in proteomics studies.

Methodology:

Accepts tab-separated peptide and protein intensity tables (not log-transformed) from MaxQuant; applies peptide- and sample-level preliminary filters; computes and compares multiple normalization methods; visualizes missing values and performs imputation; conducts differential expression comparisons and estimates statistical power.