Peptide-to-Protein Data Aggregation Using Fisher's Method Improves Target Identification in Chemical Proteomics.

Area of Science:

• Proteomics
• Statistical analysis
• Biochemistry

Background:

• Conventional protein-level statistical tests in proteomics aggregate peptide data, potentially overlooking crucial peptide-level heterogeneity.
• Alternative methods exist, such as combining peptide p-values using Fisher's method and chi-square statistics.

Purpose of the Study:

• To evaluate the efficacy of Fisher's method for combining peptide p-values in chemical proteomics.
• To compare this approach against conventional protein-level analysis across diverse datasets.

Main Methods:

• Applied Fisher's method to combine peptide p-values from chemical proteomics datasets.
• Analyzed protein expression, solubility, and protease accessibility.
• Compared results using the top four ranked peptides against protein-level aggregation.

Main Results:

• Combining the top four peptide p-values using Fisher's method consistently outperformed traditional protein-level analysis.
• This peptide-based approach mitigated biases from deviant peptides and imputation of missing values.
• Fisher's method demonstrated robustness across various proteomics assays.

Conclusions:

• Fisher's method offers a simple and robust strategy for statistical testing in proteomics.
• This method improves the identification of regulated or shifted proteins compared to aggregated protein abundance analysis.
• Utilizing peptide-level information enhances the sensitivity and reliability of proteomics studies.