Recent technological advances have enabled DNA methylation to be assayed at 
single-cell resolution. However, current protocols are limited by incomplete CpG 
coverage and hence methods to predict missing methylation states are critical to 
enable genome-wide analyses. We report DeepCpG, a computational approach based 
on deep neural networks to predict methylation states in single cells. We 
evaluate DeepCpG on single-cell methylation data from five cell types generated 
using alternative sequencing protocols. DeepCpG yields substantially more 
accurate predictions than previous methods. Additionally, we show that the model 
parameters can be interpreted, thereby providing insights into how sequence 
composition affects methylation variability.