RobNorm

RobNorm normalizes mass spectrometry proteomics data from heterogeneous samples by correcting systematic bias while preserving biological variability.

Key Features:

• Density-Power-Weight Normalization: Applies a density-power-weight method to down-weight outliers and improve robustness in heterogeneous proteomics datasets.
• Extended Robust Fitting Framework: Extends one-dimensional robust fitting methods of Windham (1995) and Fujisawa & Eguchi (2008) to structured proteomics data and incorporates a robustness criterion to minimize systematic bias without reducing tissue-specific variation.

Scientific Applications:

• Heterogeneous Proteomics Studies: Supports normalization of large-scale mass spectrometry datasets, including Genotype-Tissue Expression (GTEx) data, where cross-tissue variability must be retained.

Methodology:

RobNorm integrates density-power-weight estimation with extended one-dimensional robust fitting to model sample-specific effects, applies a robustness criterion to guide parameter estimation, and adjusts expression measurements to remove systematic bias while maintaining biological heterogeneity.