Package: scMET 1.15.0

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]

scMET_1.15.0.tar.gz
scMET_1.15.0.zip(r-4.7)scMET_1.15.0.zip(r-4.6)scMET_1.15.0.zip(r-4.5)
scMET_1.15.0.tgz(r-4.6-x86_64)scMET_1.15.0.tgz(r-4.6-arm64)scMET_1.15.0.tgz(r-4.5-x86_64)scMET_1.15.0.tgz(r-4.5-arm64)
scMET_1.15.0.tar.gz(r-4.7-arm64)scMET_1.15.0.tar.gz(r-4.7-x86_64)scMET_1.15.0.tar.gz(r-4.6-arm64)scMET_1.15.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html
card.svg |card.png
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-1.15.0(bioc 3.24)scMET-1.14.0(bioc 3.23)

immunooncologydnamethylationdifferentialmethylationdifferentialexpressiongeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionbayesiansequencingcoveragesinglecellbayesian-inferencegeneralised-linear-modelsheterogeneityhierarchical-modelsmethylation-analysissingle-cellcpp

6.32 score 25 stars 42 scripts 318 downloads 16 exports 96 dependencies

Last updated from:be256cc311. Checks:12 NOTE, 1 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE232
linux-devel-arm64NOTE324
linux-devel-x86_64NOTE405
source / vignettesOK396
linux-release-arm64NOTE337
linux-release-x86_64NOTE347
macos-release-arm64NOTE217
macos-release-x86_64NOTE477
macos-oldrel-arm64NOTE203
macos-oldrel-x86_64NOTE414
windows-develNOTE520
windows-releaseNOTE477
windows-oldrelNOTE499
wasm-releaseFAIL152

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:abindassertthatbackportsbase64encBHBiobaseBiocGenericsBiocManagerBiocStylebookdownbslibcachemcallrcheckmateclicodacowplotcpp11data.tableDelayedArraydescdigestdistributionaldplyrevaluatefarverfastmapfontawesomefsgenericsGenomicRangesggplot2gluegridExtragtablehighrhtmltoolsinlineIRangesisobandjquerylibjsonliteknitrlabelinglatticelifecyclelogitnormloomagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemimenumDerivpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrmarkdownrstanrstantoolsS4ArraysS4VectorsS7sassscalesSeqinfoSingleCellExperimentSparseArrayStanHeadersSummarizedExperimenttensorAtibbletidyselecttinytexutf8vctrsVGAMviridisviridisLitewithrxfunXVectoryaml

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

Rendered fromscMET_vignette.Rmdusingknitr::rmarkdownon May 30 2026.

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