A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free...

Area of Science:

• Proteomics
• Bioinformatics
• Data Science

Background:

• Label-free proteomics data often suffers from heterogeneity and missing values.
• Effective normalization and imputation are crucial for robust downstream analysis.
• Selecting data-specific methods is critical for accurate protein expression profiling.

Purpose of the Study:

• To identify optimal normalization and imputation method combinations for label-free proteomics data.
• To enhance quality control and the accurate identification of differentially expressed proteins.
• To provide a user-friendly tool for researchers to apply these methods.

Main Methods:

• Integrated three normalization methods (LOESS, VSN, RLR) with three imputation methods (k-NN, LLS, SVD) to create nine combinations.
• Utilized statistical measures (PCV, PEV, PMAD) to assess variation and select optimal combinations.
• Validated the approach using spiked-in standard label-free proteomics benchmark data sets.

Main Results:

• The developed approach identified specific normalization and imputation combinations that minimized intragroup and intergroup variation.
• The selected combinations demonstrated improved performance, indicated by low Normalized Root Mean Square Error (NRMSE).
• The method showed enhanced accuracy in identifying spiked-in proteins in benchmark data sets.

Conclusions:

• The study successfully identified optimal normalization and imputation strategies for label-free proteomics data.
• The 'lfproQC' R package and Shiny web application offer a valuable resource for researchers.
• The developed tool facilitates improved data quality control and accurate differential protein expression analysis.