In this response, we address key points by commentators on our previous article, "Unsupervised Machine Learning for Differential Analysis in Proteomics" (DOI: 10.1021/acs.analchem.5c03117), concerning the choice and characteristics of statistical testing and machine learning (ML) in differential proteomics. We clarify that while certain ML methods are statistically grounded, many operate on distinct nonparametric principles, offering an alternative approach when data violate standard distributional assumptions or exhibit complex multivariate structures. We also want to clarify our position that ML is proposed not as a replacement for established statistical frameworks but as a valuable expansion of the analytical toolbox, particularly useful in exploratory analysis or with heterogeneous data. We emphasize methodological pluralism, advocating for the combined use of ML and statistical methods across different stages of research, from hypothesis generation to confirmatory testing, to better address the diverse challenges in precision proteomics and to enrich biological discovery.
Xu et al. (Thu,) studied this question.