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Fig. 1 | Genome Biology

Fig. 1

From: DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Fig. 1

DeepCpG model training and applications. a Sparse single-cell CpG profiles as obtained from scBS-seq [5] or scRRBS-seq [68]. Methylated CpG sites are denoted by ones, un-methylated CpG sites by zeros, and question marks denote CpG sites with unknown methylation state (missing data). b Modular architecture of DeepCpG. The DNA module consists of two convolutional and pooling layers to identify predictive motifs from the local sequence context and one fully connected layer to model motif interactions. The CpG module scans the CpG neighbourhood of multiple cells (rows in b) using a bidirectional gated recurrent network (GRU) [36], yielding compressed features in a vector of constant size. The Joint module learns interactions between higher-level features derived from the DNA and CpG modules to predict methylation states in all cells. c, d The trained DeepCpG model can be used for different downstream analyses, including genome-wide imputation of missing CpG sites (c) and the discovery of DNA sequence motifs that are associated with DNA methylation levels or cell-to-cell variability (d)

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