title: DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.
abstract: 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.
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. cangermueller@ebi.ac.uk.
authors.forename: Christof
authors.initials: C
authors.lastname: Angermueller
authors.affiliation: Epigenetics Programme, Babraham Institute, Cambridge, UK.|Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
authors.forename: Heather J
authors.initials: HJ
authors.lastname: Lee
authors.affiliation: Epigenetics Programme, Babraham Institute, Cambridge, UK.|Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
authors.forename: Wolf
authors.initials: W
authors.lastname: Reik
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. oliver.stegle@ebi.ac.uk.
authors.forename: Oliver
authors.identifier: http://orcid.org/0000-0003-0507-1839
authors.initials: O
authors.lastname: Stegle
keywords: Artificial neural network; DNA methylation; Deep learning; Epigenetics; Machine learning; Single-cell genomics