Package: scMET 1.9.0

Andreas C. Kapourani

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:Andreas C. Kapourani [aut, cre], John Riddell [ctb]

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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

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • 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-cellcpp

6.23 score 20 stars 42 scripts 161 downloads 16 exports 107 dependencies

Last updated 4 months agofrom:e01d7d48a1. Checks:1 OK, 10 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 29 2025
R-4.5-win-x86_64NOTEJan 29 2025
R-4.5-mac-x86_64NOTEJan 29 2025
R-4.5-mac-aarch64NOTEJan 29 2025
R-4.5-linux-x86_64NOTEJan 29 2025
R-4.4-win-x86_64NOTEJan 29 2025
R-4.4-mac-x86_64NOTEJan 29 2025
R-4.4-mac-aarch64NOTEJan 29 2025
R-4.3-win-x86_64NOTEJan 29 2025
R-4.3-mac-x86_64NOTEJan 29 2025
R-4.3-mac-aarch64NOTEJan 29 2025

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.utilsutf8vctrsVGAMviridisviridisLitewithrxfunXVectoryaml

scMET Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

Rendered fromscMET_vignette.Rmdusingknitr::rmarkdownon Jan 29 2025.

Last update: 2022-05-30
Started: 2022-03-23