Analyzing single-cell bisulfite sequencing data with scbs

Abstract

Single-cell bisulfite sequencing (scBS) is a technique that enables the assessment of DNA methylation at single-base pair and single-cell resolution. The analysis of large datasets obtained from scBS requires preprocessing to reduce data size, improve signal-to-noise ratio, and provide interpretability. Typically, this is achieved by dividing the genome into large tiles and averaging the methylation signals within each tile. Here, we demonstrate that this coarse-graining approach can lead to signal dilution. As an alternative, we propose improved strategies to identify more informative regions for methylation quantification, and a more accurate quantitation method than simple averaging. Our approach enables better discrimination of cell types and other features of interest and reduces the need for large numbers of cells. We also present an approach to detect differentially methylated regions (DMRs) between groups of cells, and demonstrate its ability to identify biologically meaningful regions that are associated with genes involved in the core functions of specific cell types. To facilitate the analysis of scBS data, we have developed a software tool called Meth-SCAn that implements these methods and provides additional functionality.