Fig. 2

DeepCpG accurately predicts single-cell CpG methylation states. a Genome-wide prediction performance for imputing CpG sites in 18 serum-grown mouse embryonic stem cells (mESCs) profiled using scBS-seq [5]. Performance is measured by the area under the receiver-operating characteristic curve (AUC), using holdout validation. Considered were DeepCpG and random forest classifiers trained either using DNA sequence and CpG features (RF) or using additional annotations from corresponding cell types (RF Zhang [12]). Additionally, two baseline methods were considered, which estimate methylation states by averaging observed methylation states, either across consecutive 3-kbp regions within individual cells (WinAvg [5]) or across cells at a single CpG site (CpGAvg). b Performance breakdown of DeepCpG and RF, comparing the full models to models trained using either only methylation features (DeepCpG CpG, RF CpG) or only DNA features (DeepCpG DNA, RF DNA). c AUC of the methods as in (a) stratified by genomic contexts with increasing CpG coverage across cells. Trend lines were fit using local polynomial regression (LOESS [72]); shaded areas denote 95% confidence intervals. d AUC for alternative sequence contexts with All corresponding to genome-wide performance as in (a). e Genome-wide prediction performance on 12 2i-grown mESCs profiled using scBS-seq [5], as well as three cell types profiled using scRRBS-seq [8], including 25 human hepatocellular carcinoma cells (HCC), six HepG2 cells, and six additional mESCs. CGI CpG island, LMR low-methylated region, TSS transcription start site