Imputing not available values in single-cell DNA methylation data using the median is straightforward and effective

Area of Science:

• Epigenetics
• Genomics
• Computational Biology

Background:

• Single-cell DNA methylation analysis offers high-resolution insights into cellular epigenetics.
• A standard analysis workflow involves genome binning and calculating average methylation levels.
• Handling missing data (NA values) due to limited captured sites is crucial for preprocessing.

Purpose of the Study:

• To evaluate existing imputation methods for single-cell DNA methylation data.
• To identify an effective and theoretically sound imputation strategy.
• To provide a reliable preprocessing step for downstream epigenetic analyses.

Main Methods:

• Experimental validation of imputation techniques.
• Theoretical analysis of imputation method performance.
• Comparison of median imputation against other common methods like zero and mean imputation.

Main Results:

• Median imputation demonstrated superior performance in reflecting true methylation states.
• Both experimental and theoretical analyses supported the efficacy of median imputation.
• The proposed method provides a robust foundation for subsequent analyses.

Conclusions:

• Median imputation is a simple yet effective strategy for handling NA values in single-cell DNA methylation data.
• This approach enhances the accuracy and reliability of epigenetic analyses at single-cell resolution.
• The findings offer practical guidance for researchers in the field of epigenetics.