Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data.

ABSTRACT: Single-cell DNA methylation data has become increasingly abundant and has uncovered many genes with a positive correlation between expression and promoter methylation, challenging the common dogma based on bulk data. However, computational tools for analyzing single-cell methylome data 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

SUBMITTER: Uzun Y 

PROVIDER: S-EPMC7849382 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature