Software

scMET

scMET is a hierarchical Bayesian model that shares information across cells and genomic features to robustly quantify genuine biological single-cell methylation heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses.

SeuratPipe

SeuratPipe streamlines computational analysis of single-cell genomics data by wraping common tasks in easy to run pipelines. Currently supports sc/snRNA-seq and Visium data analysis.

BPRMeth

BPRMeth is a probabilistic method to extract higher order features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression or clustering genomic regions or cells according to their methylation profiles.

Melissa

Melissa is a Baysian probabilistic model for jointly clustering and imputing single-cell methylation data. This is done by taking into account local correlations via a generalised linear model and global similarities using a mixture modelling approach.

Analysis