RT Journal Article
SR Electronic
T1 Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.05.137000
DO 10.1101/2020.06.05.137000
A1 Yasin Uzun
A1 Hao Wu
A1 Kai Tan
YR 2020
UL http://biorxiv.org/content/early/2020/06/06/2020.06.05.137000.abstract
AB Despite rapid advances in single-cell DNA methylation profiling methods, computational tools for data analysis are lagging far behind. A number of tasks, including cell type calling and integration with transcriptome data, requires the construction of a robust gene activity matrix as the prerequisite but challenging task. The advent of multi-omics data enables measurement of both DNA methylation and gene expression for the same single cells. Although such data is rather sparse, they are sufficient to train supervised models that capture the complex relationship between DNA methylation and gene expression and predict gene activities at single-cell level. Here, we present MAPLE (Methylome Association by Predictive Linkage to Expression), a computational framework that learns the association between DNA methylation and expression using both gene- and cell-dependent statistical features. Using multiple datasets generated with different experimental protocols, we show that using predicted gene activity values significantly improves several analysis tasks, including clustering, cell type identification and integration with transcriptome data. With the rapid accumulation of single-cell epigenomics data, MAPLE provides a general framework for integrating such data with transcriptome data.Competing Interest StatementThe authors have declared no competing interest.