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betaclust

The goal of betaclust is to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of BMMs employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.

Installation

You can install the development version of betaclust like so:

library(devtools)
install_github('koyelucd/betaclust',force = TRUE)
library(betaclust)

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Version

Install

install.packages('betaclust')

Monthly Downloads

254

Version

1.0.4

License

GPL-3

Maintainer

Koyel Majumdar

Last Published

September 24th, 2024

Functions in betaclust (1.0.4)

beta_kn

Fit the KN. model
AUC_WD_metric

AUC and WD function
ecdf.betaclust

The empirical cumulative distribution function plot
beta_k

Fit the K.. model
DMC_identification

The DMC identification function
beta_kr

Fit the K.R Model
em_aic

Akaike Information Criterion
plot.betaclust

Plots for visualizing the betaclust class object
em_icl

Integrated Complete-data Likelihood (ICL) Criterion
em_bic

Bayesian Information Criterion
legacy.data

MethylationEPIC manifest data.
pca.methylation.data

DNA methylation data from patients with prostate cancer
betaclust

The betaclust wrapper function
threshold

Thresholds under the K.. and the KN. models
summary.betaclust

Summarizing the beta mixture model fits