CaMelia: imputation in single-cell methylomes based on local similarities between cells

Motivation: Single-cell DNA methylation sequencing detects methylation levels with single-cell resolution, while this technology is upgrading our understanding of the regulation of gene expression through epigenetic modifications. Meanwhile, almost all current technologies suffer from the inherent problem of detecting low coverage of the number of CpGs. Therefore, addressing the inherent sparsity of raw data is essential for quantitative analysis of the whole genome. Results: Here, we reported CaMelia, a CatBoost gradient boosting method for predicting the missing methylation states based on the locally paired similarity of intercellular methylation patterns. On real single-cell methylation datasets, CaMelia yielded significant imputation performance gains over previous methods. Furthermore, applying the imputed data to the downstream analysis of cell-type identification, we found that CaMelia helped to discover more intercellular differentially methylated loci that were masked by the sparsity in raw data, and the clustering results demonstrated that CaMelia could preserve cell-cell relationships and improve the identification of cell types and cell subpopulations.

Automated summary

CaMelia, a CatBoost gradient boosting method, predicts missing methylation states based on locally paired intercellular methylation similarity. It significantly improves imputation performance on real single-cell methylation datasets, helping to discover intercellular differentially methylated loci masked by sparsity in raw data. The results demonstrate that CaMelia preserves cell-cell relationships, enhances cell type identification, and improves subpopulation detection.