em_aic

Compute the AIC value for the optimal model.

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

em_aic function

<dl><dt>llk</dt>
<dd>Log-likelihood value.</dd>
<dt>C</dt>
<dd>Number of CpG sites.</dd>
<dt>M</dt>
<dd>Number of methylation states identified in a DNA sample.</dd>
<dt>N</dt>
<dd>Number of patients.</dd>
<dt>R</dt>
<dd>Number of DNA sample types collected from each patient.</dd>
<dt>model_name</dt>
<dd>Fitted mixture model. Options are "K..", "KN." and/or "K.R" (default = "K..").</dd></dl>

Arguments

Akaike Information Criterion — em_aic

<dl>

<dt>llk</dt>
<dd>Log-likelihood value.</dd>


<dt>C</dt>
<dd>Number of CpG sites.</dd>


<dt>M</dt>
<dd>Number of methylation states identified in a DNA sample.</dd>


<dt>N</dt>
<dd>Number of patients.</dd>


<dt>R</dt>
<dd>Number of DNA sample types collected from each patient.</dd>


<dt>model_name</dt>
<dd>Fitted mixture model. Options are "K..", "KN." and/or "K.R" (default = "K..").</dd>

</dl>

em_aic: Akaike Information Criterion

Description

Usage

Value

Arguments

Details

See Also