Package: Melissa 1.21.0

C. A. Kapourani

Melissa: Bayesian clustering and imputationa of single cell methylomes

Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach.

Authors:C. A. Kapourani [aut, cre]

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Melissa/json (API)
NEWS

# Install 'Melissa' in R:

install.packages('Melissa', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

melissa_encode_dt - Synthetic ENCODE single cell methylation data
melissa_synth_dt - Synthetic single cell methylation data

On BioConductor:Melissa-1.21.0(bioc 3.20)Melissa-1.20.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

13 exports 1.51 score 102 dependencies 4 mentions

Last updated 2 months agofrom:b2edeb79a4

Exports:binarise_files create_melissa_data_obj eval_cluster_performance eval_imputation_performance filter_by_coverage_across_cells filter_by_cpg_coverage filter_by_variability impute_met_files impute_test_met melissa melissa_gibbs partition_dataset plot_melissa_profiles

Dependencies:askpass assertthat base64enc BiocGenerics BiocManager BiocStyle bitops bookdown BPRMeth bslib cachem caTools class cli coda codetools colorspace cowplot curl data.table digest doParallel e1071 earth evaluate fansi farver fastmap fontawesome foreach Formula fs GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 glue gplots gtable gtools highr htmltools httr IRanges isoband iterators jquerylib jsonlite kernlab KernSmooth knitr labeling lattice lifecycle magrittr MASS Matrix matrixcalc MatrixModels mclust mcmc MCMCpack memoise mgcv mime munsell mvtnorm nlme openssl pillar pkgconfig plotmo plotrix proxy quantreg R6 randomForest rappdirs RColorBrewer Rcpp RcppArmadillo rlang rmarkdown ROCR S4Vectors sass scales SparseM survival sys tibble tinytex truncnorm UCSC.utils utf8 vctrs viridisLite withr xfun XVector yaml zlibbioc

Process and filter scBS-seq data

Andreas C. Kapourani, Guido Sanguinetti

Rendered fromprocess_files.Rmdusingknitr::rmarkdownon Jul 01 2024.

Last update: 2020-06-10
Started: 2019-02-15

Cluster and impute scBS-seq data using Melissa

Andreas C. Kapourani, Guido Sanguinetti

Rendered fromrun_melissa.Rmdusingknitr::rmarkdownon Jul 01 2024.

Last update: 2020-06-10
Started: 2019-02-15

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Binarise CpG sites	binarise_files
Compute clustering ARI	cluster_ari
Compute clustering assignment error 'cluster_error' computes the clustering assignment error, i.e. the average number of incorrect cluster assignments: OE = sum_{n=1}^{N}(I(LT_{n} \neq LP_{n})) / N	cluster_error
Create methylation regions for all cells	create_melissa_data_obj
Evaluate clustering performance	eval_cluster_performance
Evaluate imputation performance	eval_imputation_performance
Extract responses y	extract_y
Filtering process prior to running Melissa	filter_by_coverage_across_cells filter_by_cpg_coverage filter_by_variability filter_cpgs, filter_regions melissa_filter
Impute/predict methylation files	impute_met_files
Impute/predict test methylation states	impute_test_met
Initialise design matrices	init_design_matrix
Compute stable log-sum-exp	log_sum_exp
Cluster and impute single cell methylomes using VB	melissa melissa_cluster, melissa_impute, melissa_vb
'Melissa': Bayesian clustering and imputation of single cell methylomes	.datatable.aware Melissa
Synthetic ENCODE single cell methylation data	melissa_encode_dt
Gibbs sampling algorithm for Melissa model	melissa_gibbs
Synthetic single cell methylation data	melissa_synth_dt
Partition synthetic dataset to training and test set	partition_dataset
Plot predictive methylaation profiles	plot_melissa_profiles