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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NEWS

# Install 'scMET' in R:
install.packages('scMET', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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-cell

6.23 score 20 stars 42 scripts 124 downloads 16 exports 108 dependencies

Last updated 19 days agofrom:e01d7d48a1. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-win-x86_64NOTEOct 31 2024
R-4.5-linux-x86_64NOTEOct 31 2024
R-4.4-win-x86_64NOTEOct 31 2024
R-4.4-mac-x86_64NOTEOct 31 2024
R-4.4-mac-aarch64NOTEOct 31 2024
R-4.3-win-x86_64NOTEOct 31 2024
R-4.3-mac-x86_64NOTEOct 31 2024
R-4.3-mac-aarch64NOTEOct 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

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

Rendered fromscMET_vignette.Rmdusingknitr::rmarkdownon Oct 31 2024.

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