School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK.
MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Genome Biol. 2019 Mar 21;20(1):61. doi: 10.1186/s13059-019-1665-8.
Measurements of single-cell methylation are revolutionizing our understanding of epigenetic control of gene expression, yet the intrinsic data sparsity limits the scope for quantitative analysis of such data. Here, we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to cluster cells based on local methylation patterns, discovering patterns of epigenetic variability between cells. The clustering also acts as an effective regularization for data imputation on unassayed CpG sites, enabling transfer of information between individual cells. We show both on simulated and real data sets that Melissa provides accurate and biologically meaningful clusterings and state-of-the-art imputation performance.
单细胞甲基化的测量正在彻底改变我们对基因表达的表观遗传控制的理解，但内在的数据稀疏性限制了对这些数据进行定量分析的范围。在这里，我们引入了 Melissa（单细胞分析的甲基化推断），这是一种基于局部甲基化模式对细胞进行聚类的贝叶斯层次方法，发现细胞之间的表观遗传可变性模式。聚类还可以作为未测定 CpG 位点数据插补的有效正则化，从而在单个细胞之间传递信息。我们在模拟和真实数据集上都表明，Melissa 提供了准确且具有生物学意义的聚类和最先进的插补性能。
