Integrating Single-Cell Methylome and Transcriptome Data with MAPLE

Abstract

As a mechanism of epigenetic gene regulation, DNA methylation has crucial roles in developmental and differentiation programs. Thanks to the recently introduced bisulfite-sequencing-based methods, it is possible to profile the entire methylome at single-cell resolution. However, analysis of single-cell methylome data is challenging due to data sparsity and moderate correlation with transcript level. Our recently developed computational framework, MAPLE, addresses these challenges using supervised learning models. Using both genomic sequence and methylation information as the input, MAPLE predicts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data.

Keywords: DNA methylation; Data integration; Epigenomics; Multi-omics; Single-cell.