Optimizing Proteomics Data Differential Expression Analysis via High-Performing Rules and Ensemble Inference

Abstract

In the process of identifying phenotype-specific or differentially expressed proteins from proteomic data, a standard workflow consists of five key steps: raw data quantification, expression matrix construction, matrix normalization, missing data imputation, and differential expression analysis. However, due to the availability of multiple options at each step, selecting ad hoc combinations of options can result in suboptimal analysis. To address this, we conducted an extensive study involving 10,808 experiments to compare the performance of exhaustive option combinations for each step across 12 gold standard spike-in datasets and three quantification platforms: FragPipe, MaxQuant, and DIA-NN. By employing frequent pattern mining techniques on the data from these experiments, we discovered high-performing rules for selecting optimal workflows. These rules included avoiding normalization, utilizing MinProb for missing value imputation, and employing limma for differential expression analysis. We found that workflow performances were predictable and could be accurately categorized using average F1 scores and Matthews correlation coefficients, both exceeding 0.79 in 10-fold cross-validations. Furthermore, by integrating the top-ranked workflows through ensemble inference, we not only improved the accuracy of differential expression analysis (e.g., achieving a 1-5% gain under five performance metrics for FragPipe), but also enhanced the workflows ability to aggregate proteomic information across various levels, including peptide and protein level intensities and spectral counts, providing a comprehensive perspective on the data. Overall, our study highlights the importance of selecting optimal workflow combinations and demonstrates the benefits of ensemble inference in improving both the accuracy and comprehensiveness of proteomic data analysis.