Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties from heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and enables clustering of distinct biological populations, cell type annotation, differentially methylated region calling, and interpretation of results - facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell and cortex data. We further apply Amethyst to an atlas-scale brain dataset and deconvolute non-CG methylation patterns in human astrocytes and oligodendrocytes, challenging the notion that this form of methylation is principally relevant to neurons in the brain. Tools such as Amethyst will make single-cell methylation data analysis more accessible, catalyzing research progress across diverse contexts.