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.
Package details |
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Author | Koyel Majumdar [aut, cre] (<https://orcid.org/0000-0003-4692-9002>), Romina Silva [aut], Antoinette Sabrina Perry [aut], Ronald William Watson [aut], Andrea Rau [aut] (<https://orcid.org/0000-0001-6469-488X>), Florence Jaffrezic [aut], Thomas Brendan Murphy [aut] (<https://orcid.org/0000-0002-5668-7046>), Isobel Claire Gormley [aut] (<https://orcid.org/0000-0001-7713-681X>) |
Maintainer | Koyel Majumdar <koyel.majumdar@ucdconnect.ie> |
License | GPL-3 |
Version | 1.0.4 |
Package repository | View on CRAN |
Installation |
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