The importance of batch sensitization in missing value imputation.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Missing values and batch effects are common challenges in complex data analysis.
• Existing methods for missing value imputation (MVI) and batch correction often operate independently.
• The confounding impact of MVI on downstream batch correction has not been thoroughly investigated.

Purpose of the Study:

• To investigate the impact of different missing value imputation strategies on subsequent batch correction.
• To evaluate the performance of global, self-batch, and cross-batch imputation methods in the presence of batch effects.
• To provide recommendations for optimal imputation strategies in omics data analysis.

Main Methods:

• Simulated data analysis to model three imputation strategies: global (M1), self-batch (M2), and cross-batch (M3).
• Validation of simulation findings using real-world proteomics and genomics datasets.
• Assessment of batch correction efficacy and statistical error rates for each imputation strategy.

Main Results:

• The self-batch imputation strategy (M2), which considers batch covariates, significantly enhances batch correction and reduces statistical errors.
• Global (M1) and cross-batch (M3) imputation methods can lead to batch-effect dilution and irreversible increases in intra-sample noise.
• This noise introduced by non-considerate MVI is unremovable by standard batch correction algorithms, leading to false positives and negatives.

Conclusions:

• Explicitly accounting for batch effects during missing value imputation is crucial for accurate downstream analysis.
• Careless imputation strategies that ignore covariates like batch effects should be avoided to prevent data misinterpretation.
• The findings underscore the importance of integrated approaches for handling missing values and batch effects in omics data.