Package: scMET 1.9.0
scMET: Bayesian modelling of cell-to-cell DNA methylation heterogeneity
High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.
Authors:
scMET_1.9.0.tar.gz
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scMET.pdf |scMET.html✨
scMET/json (API)
NEWS
# Install 'scMET' in R: |
install.packages('scMET', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andreaskapou/scmet/issues
- scmet_diff_dt - Synthetic methylation data from two groups of cells
- scmet_dt - Synthetic methylation data from a single population
On BioConductor:scMET-1.9.0(bioc 3.21)scMET-1.8.0(bioc 3.20)
immunooncologydnamethylationdifferentialmethylationdifferentialexpressiongeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionbayesiansequencingcoveragesinglecellbayesian-inferencegeneralised-linear-modelsheterogeneityhierarchical-modelsmethylation-analysissingle-cell
Last updated 19 days agofrom:e01d7d48a1. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-win-x86_64 | NOTE | Oct 31 2024 |
R-4.5-linux-x86_64 | NOTE | Oct 31 2024 |
R-4.4-win-x86_64 | NOTE | Oct 31 2024 |
R-4.4-mac-x86_64 | NOTE | Oct 31 2024 |
R-4.4-mac-aarch64 | NOTE | Oct 31 2024 |
R-4.3-win-x86_64 | NOTE | Oct 31 2024 |
R-4.3-mac-x86_64 | NOTE | Oct 31 2024 |
R-4.3-mac-aarch64 | NOTE | Oct 31 2024 |
Exports:bb_mlecreate_design_matrixsce_to_scmetscmetscmet_differentialscmet_hvfscmet_lvfscmet_plot_efdr_efnr_gridscmet_plot_estimated_vs_truescmet_plot_mascmet_plot_mean_varscmet_plot_vf_tail_probscmet_plot_volcanoscmet_simulatescmet_simulate_diffscmet_to_sce
Dependencies:abindaskpassassertthatbackportsbase64encBHBiobaseBiocGenericsBiocManagerBiocStylebookdownbslibcachemcallrcheckmateclicodacolorspacecowplotcrayoncurldata.tableDelayedArraydescdigestdistributionaldplyrevaluatefansifarverfastmapfontawesomefsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegridExtragtablehighrhtmltoolshttrinlineIRangesisobandjquerylibjsonliteknitrlabelinglatticelifecyclelogitnormloomagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimemunsellnlmenumDerivopensslpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrmarkdownrstanrstantoolsS4ArraysS4VectorssassscalesSingleCellExperimentSparseArrayStanHeadersSummarizedExperimentsystensorAtibbletidyselecttinytexUCSC.utilsutf8vctrsVGAMviridisviridisLitewithrxfunXVectoryamlzlibbioc