A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets.

Area of Science:

• Proteomics
• Bioinformatics
• Mass Spectrometry

Background:

• Missing values in proteomic data hinder analysis and reproducibility.
• Existing imputation methods lack a universal best-fit strategy, especially for clinical data.
• Evaluating imputation for clinical data-independent mass spectrometry (DIA-MS) is challenging.

Purpose of the Study:

• To establish a strategy for assessing imputation methods on clinical label-free DIA-MS datasets.
• To evaluate eight imputation methods across different data types and quantification levels.
• To provide a framework for selecting accurate imputation strategies for differential protein analysis.

Main Methods:

• Utilized three DIA-MS datasets with real missing values: dilution series, pilot, and clinical tumor-stroma.
• Assessed eight imputation methods (e.g., LLS, RF, BPCA) at various parameters and quantification levels.
• Developed an analytical framework using smaller datasets to identify optimal methods for larger datasets.

Main Results:

• Imputation methods exploiting local data structures (LLS, RF) performed well on dilution series data.
• Methods using global data structures (BPCA) excelled on pilot and clinical datasets.
• Imputing at the fragment level enhanced accuracy and protein quantification numbers.

Conclusions:

• The optimal imputation method is data-structure dependent.
• The proposed analytical framework efficiently identifies accurate imputation strategies.
• This approach aids in reproducible differential protein analysis of clinical DIA-MS data.