ArticleGenome biology2019
Melissa: Bayesian clustering and imputation of single-cell methylomes.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- SMORE: joint dimension reduction and cell population discovery on single-cell methylome data.bioRxiv : the preprint server for biology · 2026Article
- A systematic benchmarking framework and dual-view optimization strategy for single-cell DNA methylation imputation.Briefings in bioinformatics · 2026Article
- Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework.Nature communications · 2026Article
- mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data.Nature communications · 2026Article
- Single-cell DNA methylation analysis tool Amethyst resolves distinct non-CG methylation patterns in human astrocytes and oligodendrocytes.Communications biology · 2025Article
- MambaCpG: an accurate model for single-cell DNA methylation status imputation using mamba.Briefings in bioinformatics · 2025Article
- Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.Cell genomics · 2025Article
- Regulated GATA1 expression as a universal gene therapy for Diamond-Blackfan anemia.Cell stem cell · 2025Article
- Statistical modeling of immunoprecipitation efficiency of MeRIP-seq data enabled accurate detection and quantification of epitranscriptome.Computational and structural biotechnology journal · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- vmrseq: probabilistic modeling of single-cell methylation heterogeneity.Genome biology · 2024Article
- Analyzing single-cell bisulfite sequencing data with MethSCAn.Nature methods · 2024Article
- scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia.bioRxiv : the preprint server for biology · 2024Article
- Estimating genome-wide DNA methylation heterogeneity with methylation patterns.Epigenetics & chromatin · 2023Article
- GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs.Bioinformatics (Oxford, England) · 2023Article
- iCpG-Pos: an accurate computational approach for identification of CpG sites using positional features on single-cell whole genome sequence data.Bioinformatics (Oxford, England) · 2023Article
- Computational Methods for Single-cell DNA Methylome Analysis.Genomics, proteomics & bioinformatics · 2023Review
- Methylome Imputation by Methylation Patterns.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Single-Cell DNA Methylation Analysis in Cancer.Cancers · 2022Review
- scSPLAT, a scalable plate-based protocol for single cell WGBS library preparation.Scientific reports · 2022Article
Corrections and comments
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2 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.