AUC_WD_metric

Function to find the level of similarities between the \(R\) cumulative distributions estimated in each of the \(K\) clusters.

A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <doi:10.48550/arXiv.2211.01938> 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 beta mixture models 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.

Koyel Majumdar

betaclust

A Family of Beta Mixture Models for Clustering Beta-Valued DNA
Methylation Data

Romina Silva

Antoinette Sabrina Perry

Ronald William Watson

Andrea Rau

Florence Jaffrezic

Thomas Brendan Murphy

Isobel Claire Gormley

AUC_WD_metric function

<dl><dt>alpha</dt>
<dd>The first shape parameter estimated for the beta mixture model.</dd>
<dt>delta</dt>
<dd>The second shape parameter estimated for the beta mixture model.</dd>
<dt>K</dt>
<dd>The number of clusters estimated.</dd>
<dt>R</dt>
<dd>The number of sample types in the dataset.</dd></dl>

Arguments

AUC and WD function — AUC_WD_metric

<dl>

<dt>alpha</dt>
<dd>The first shape parameter estimated for the beta mixture model.</dd>


<dt>delta</dt>
<dd>The second shape parameter estimated for the beta mixture model.</dd>


<dt>K</dt>
<dd>The number of clusters estimated.</dd>


<dt>R</dt>
<dd>The number of sample types in the dataset.</dd>

</dl>

AUC_WD_metric: AUC and WD function

Description

Usage

Value

Arguments

Details

See Also