SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Quality control in quantitative proteomics is crucial but challenging, especially for outlier identification.
• Unsupervised learning offers solutions but can be compromised by lack of labels and model randomness.
• Single models risk high false positives, necessitating robust methods for accurate outlier detection.

Purpose of the Study:

• To introduce SEAOP, a Python toolbox designed for robust outlier detection in quantitative proteomics.
• To leverage ensemble modeling for improved accuracy and mitigation of randomness in unsupervised learning.
• To provide an intuitive visualization strategy for outlier and non-outlier sample distribution.

Main Methods:

• SEAOP employs multi-round resampling to generate diverse data subsets for analysis.
• Outlier candidates are identified in each subset using various detection methods.
• A chi-square test at a 95% confidence level aggregates candidates into confirmed outliers.
• Optimal hyperparameters were determined using a gradient-simulated dataset and Mann-Kendall trend test.

Main Results:

• SEAOP demonstrated reliability and accuracy in identifying outliers across three experimental quantitative proteomics datasets.
• The ensemble mechanism effectively reduced false positives compared to single models.
• The integrated visualization strategy clearly presented sample distributions.

Conclusions:

• SEAOP provides a reliable and accurate solution for quality control in quantitative proteomics.
• The toolbox effectively addresses the challenge of outlier detection using unsupervised ensemble learning.
• SEAOP's methods enhance the precision and interpretability of quantitative proteomics data analysis.