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✨
DESCRIPTION |NEWS
card.svg |card.png
scMET/json (API)

# 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)

immunooncology dnamethylation differentialmethylation differentialexpression geneexpression generegulation epigenetics genetics clustering featureextraction regression bayesian sequencing coverage singlecell bayesian-inference generalised-linear-models heterogeneity hierarchical-models methylation-analysis single-cell cpp

6.31 score 25 stars 41 scripts 16 exports 97 dependencies

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

Target	Result	Time
bioc-checks	NOTE	236
linux-devel-arm64	NOTE	357
linux-devel-x86_64	NOTE	402
source / vignettes	OK	385
linux-release-arm64	NOTE	376
linux-release-x86_64	NOTE	390
macos-release-arm64	NOTE	207
macos-release-x86_64	NOTE	616
macos-oldrel-arm64	NOTE	189
macos-oldrel-x86_64	NOTE	667
windows-devel	NOTE	430
windows-release	NOTE	435
windows-oldrel	NOTE	446
wasm-release	FAIL	198

Exports:bb_mle create_design_matrix sce_to_scmet scmet scmet_differential scmet_hvf scmet_lvf scmet_plot_efdr_efnr_grid scmet_plot_estimated_vs_true scmet_plot_ma scmet_plot_mean_var scmet_plot_vf_tail_prob scmet_plot_volcano scmet_simulate scmet_simulate_diff scmet_to_sce

Dependencies:abind assertthat backports base64enc BH Biobase BiocGenerics BiocManager BiocStyle bookdown bslib cachem callr checkmate cli coda cowplot cpp11 data.table DelayedArray desc digest distributional dplyr evaluate farver fastmap fontawesome fs generics GenomicRanges ggplot2 glue gridExtra gtable highr htmltools inline IRanges isoband jquerylib jsonlite knitr labeling lattice lifecycle logitnorm loo magrittr MASS Matrix MatrixGenerics matrixStats memoise mime numDeriv otel pillar pkgbuild pkgconfig posterior processx ps QuickJSR R6 rappdirs RColorBrewer Rcpp RcppEigen RcppParallel rlang rmarkdown rstan rstantools S4Arrays S4Vectors S7 sass scales Seqinfo SingleCellExperiment SparseArray StanHeaders SummarizedExperiment tensorA tibble tidyselect tinytex utf8 vctrs VGAM viridis viridisLite withr xfun XVector yaml

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

Rendered fromscMET_vignette.Rmdusingknitr::rmarkdown

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

Help page	Topics
'scMET': Bayesian modelling of DNA methylation at single-cell resolution.	scMET-package
Beta binomial maximum likelihood estimation (BB MLE)	bb_mle
Create design matrix	create_design_matrix
Convert from SingleCellExperiment to scmet object	sce_to_scmet
Perform inference with scMET	scMET scmet scmet_mcmc scmet_vb
Synthetic methylation data from two groups of cells	scmet_diff_dt
Differential testing using scMET	differential_methylation, differential_test, differential_variability scmet_differential
Synthetic methylation data from a single population	scmet_dt
Detect highly (or lowly) variable features with scMET	detect_hvf detect_hvf_lvf detect_lvf scmet_hvf scmet_hvf_lvf scmet_lvf
Plot EFDR/EFNR grid	scmet_plot_efdr_efnr_grid
Plot true versus inferred parameter estimated.	scmet_plot_estimated_vs_true
MA plot for differential analysis	scmet_plot_ma
Plotting mean-variability relationship	scmet_plot_mean_var
Plot tail probabilities for variable feature analysis	scmet_plot_vf_tail_prob
Volcano plot for differential analysis	scmet_plot_volcano
Simulate methylation data from scMET.	scmet_simulate
Simulate differential methylation data from scMET.	scmet_simulate_diff
Convert from scmet to SingleCellExperiment object.	scmet_to_sce

Package: scMET 1.15.0

scMET: Bayesian modelling of cell-to-cell DNA methylation heterogeneity

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