RT Journal Article
SR Electronic
T1 Accurate prediction of single-cell DNA methylation states using deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 055715
DO 10.1101/055715
A1 Christof Angermueller
A1 Heather J. Lee
A1 Wolf Reik
A1 Oliver Stegle
YR 2017
UL http://biorxiv.org/content/early/2017/02/01/055715.abstract
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.