Response to "Comment on 'Unsupervised Machine Learning for Differential Analysis in Proteomics' ".

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Commentators raised questions regarding statistical testing and machine learning (ML) in differential proteomics.
• The previous article, "Unsupervised Machine Learning for Differential Analysis in Proteomics," explored ML applications.

Purpose of the Study:

• To address commentators' key points on statistical testing and ML in differential proteomics.
• To clarify the position of ML as an expansion of the analytical toolbox, not a replacement for statistical methods.

Main Methods:

• Discussion and clarification of statistical principles underlying certain ML methods.
• Explanation of nonparametric principles utilized by ML for data violating standard distributional assumptions.
• Highlighting the utility of ML in exploratory analysis and with heterogeneous data.

Main Results:

• Certain ML methods are statistically grounded, while others use distinct nonparametric principles.
• ML provides valuable alternatives when data exhibit complex multivariate structures or violate distributional assumptions.
• ML is particularly useful for exploratory analysis and heterogeneous datasets in proteomics.

Conclusions:

• Methodological pluralism is advocated, combining ML and statistical methods for comprehensive analysis.
• The integrated use of ML and statistics enhances precision proteomics and enriches biological discovery.
• ML serves as a valuable expansion to the analytical toolbox for differential proteomics.