Unsupervised Machine Learning for Differential Analysis in Proteomics

Differential analysis in proteomics is pivotal for biomarker
discovery
and disease mechanism elucidation, yet traditional statistical methods
are constrained by distributional assumptions and empirical fold change
threshold dependencies. This study systematically evaluates 18 unsupervised
anomaly detection machine learning (ML) algorithms against the established
statistical frameworks for differential protein detection from proteomic
data sets. Using in silico simulated data sets derived
from experimental data, we enabled cross-algorithm comparability through
a probability based transformation. Results demonstrated that ML methods,
particularly the Minimum Covariance Determinant (MCD), outperformed
statistical test in recall, precision, and accuracy, with superior
robustness to intersample heterogeneity. Validation on real-world
proteomic data further confirmed that the MCD-identified differentially
expressed proteins comprehensively covered canonical pathways while
uncovering novel tumor-associated functional biomolecules. This work
establishes unsupervised ML methods as robust alternatives to traditional
hypothesis-driven statistical approaches in proteomics differential
analysis, offering enhanced reliability for precision medicine research.