PT  - JOURNAL ARTICLE
AU  - Angermueller, Christof
AU  - Lee, Heather J.
AU  - Reik, Wolf
AU  - Stegle, Oliver
TI  - Accurate prediction of single-cell DNA methylation states using deep learning
AID  - 10.1101/055715
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 055715
4099  - http://biorxiv.org/content/early/2017/02/01/055715.short
4100  - http://biorxiv.org/content/early/2017/02/01/055715.full
AB  - Recent technological advances have enabled assaying DNA methylation at single-cell resolution. Current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. Here, we report DeepCpG, a computational approach based on deep neural networks to predict DNA methylation states from DNA sequence and incomplete methylation profiles in single cells. We evaluated DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols, finding that DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the parameters of our model can be interpreted, thereby providing insights into the effect of sequence composition on methylation variability.