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

Songming Tang; Siyu Li; Shengquan Chen

doi:10.1002/qub2.70000

Quant. Biol. ›› 2025, Vol. 13 ›› Issue (3) : e70000 DOI: 10.1002/qub2.70000

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

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Abstract

Recent advances in single-cell DNA methylation have provided unprecedented opportunities to explore cellular epigenetic differences with maximal resolution. A common workflow for single-cell DNA methylation analysis is binning the genome into multiple regions and computing the average methylation level within each region. In this process, imputing not available (NA) values which are caused by the limited number of captured methylation sites is a necessary preprocessing step for downstream analyses. Existing studies have employed several simple imputation methods (such as zeros imputation or means imputation), however, there is a lack of theoretical studies or benchmark tests of these approaches. Through both experiments and theoretical analysis, we found that using the medians to impute NA values can effectively and simply reflect the methylation state of the NA values, providing an accurate foundation for downstream analyses.

Keywords

data imputation / single-cell DNA methylation

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Songming Tang, Siyu Li, Shengquan Chen. Imputing not available values in single-cell DNA methylation data using the median is straightforward and effective. Quant. Biol., 2025, 13(3): e70000 DOI:10.1002/qub2.70000

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References

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[1]	Ahn J , Heo S , Lee J , Bang D . Introduction to single-cell DNA methylation profiling methods. Biomolecules. 2021; 11 (7): 1013.

[2]	Lister R , Pelizzola M , Dowen RH , Hawkins RD , Hon G , Tonti-Filippini J , et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009; 462 (7271): 315- 22.

[3]	Liu H , Zhou J , Tian W , Luo C , Bartlett A , Aldridge A , et al. DNA methylation atlas of the mouse brain at single-cell resolution. Nature. 2021; 598 (7879): 120- 8.

[4]	Danese A , Richter ML , Chaichoompu K , Fischer DS , Theis FJ , Colomé-Tatché M . EpiScanpy: integrated single-cell epigenomic analysis. Nat Commun. 2021; 12 (1): 5228.

[5]	Iqbal W , Zhou W . Computational methods for single-cell DNA methylome analysis. Genom Proteom Bioinform. 2023; 21 (1): 48- 66.

[6]	Luo C , Rivkin A , Zhou J , Sandoval JP , Kurihara L , Lucero J , et al. Robust single-cell DNA methylome profiling with snmC-seq2. Nat Commun. 2018; 9 (1): 3824.

[7]	Kremer LP , Martina MB , Svetlana O , Leonie K , Santiago C , Ana MV , et al. Analyzing single-cell bisulfite sequencing data with MethSCAn. Nat Methods. 2024; (21): 1616- 23.

[8]	Tian W , Zhou J , Bartlett A , Zeng Q , Liu H , Castanon RG , et al. Single-cell DNA methylation and 3D genome architecture in the human brain. Science. 2023; 382 (6667): eadf5357.

[9]	Tang S , Cui X , Wang R , Li S , Li S , Huang X , et al. scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data. Nat Commun. 2024; 15 (1): 1629.

[10]	Shengquan C , Boheng Z , Xiaoyang C , Xuegong Z , Rui J . stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics. Bioinformatics. 2021; 37 (Suppl_1): i299- 307.

[11]	Acharya SN , Nichols RV , Rylaarsdam LE , O’Connell BL , Braun TP , Adey AC . sciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden. Genome Biol. 2024; 25 (1): 186.