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Clustering Gene Expressions Using the Table Invitation Prior

A prior for Bayesian nonparametric clustering called the Table Invitation Prior (TIP) is used to cluster gene expression data. TIP uses information concerning the pairwise distances between subjects (e.g., gene expression samples) and automatically estimates the number of clusters. TIP’s hyperparame...

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Autores principales: Harrison, Charles W., He, Qing, Huang, Hsin-Hsiung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9690110/
https://www.ncbi.nlm.nih.gov/pubmed/36360270
http://dx.doi.org/10.3390/genes13112036
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author Harrison, Charles W.
He, Qing
Huang, Hsin-Hsiung
author_facet Harrison, Charles W.
He, Qing
Huang, Hsin-Hsiung
author_sort Harrison, Charles W.
collection PubMed
description A prior for Bayesian nonparametric clustering called the Table Invitation Prior (TIP) is used to cluster gene expression data. TIP uses information concerning the pairwise distances between subjects (e.g., gene expression samples) and automatically estimates the number of clusters. TIP’s hyperparameters are estimated using a univariate multiple change point detection algorithm with respect to the subject distances, and thus TIP does not require an analyst’s intervention for estimating hyperparameters. A Gibbs sampling algorithm is provided, and TIP is used in conjunction with a Normal-Inverse-Wishart likelihood to cluster 801 gene expression samples, each of which belongs to one of five different types of cancer.
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spelling pubmed-96901102022-11-25 Clustering Gene Expressions Using the Table Invitation Prior Harrison, Charles W. He, Qing Huang, Hsin-Hsiung Genes (Basel) Article A prior for Bayesian nonparametric clustering called the Table Invitation Prior (TIP) is used to cluster gene expression data. TIP uses information concerning the pairwise distances between subjects (e.g., gene expression samples) and automatically estimates the number of clusters. TIP’s hyperparameters are estimated using a univariate multiple change point detection algorithm with respect to the subject distances, and thus TIP does not require an analyst’s intervention for estimating hyperparameters. A Gibbs sampling algorithm is provided, and TIP is used in conjunction with a Normal-Inverse-Wishart likelihood to cluster 801 gene expression samples, each of which belongs to one of five different types of cancer. MDPI 2022-11-04 /pmc/articles/PMC9690110/ /pubmed/36360270 http://dx.doi.org/10.3390/genes13112036 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Harrison, Charles W.
He, Qing
Huang, Hsin-Hsiung
Clustering Gene Expressions Using the Table Invitation Prior
title Clustering Gene Expressions Using the Table Invitation Prior
title_full Clustering Gene Expressions Using the Table Invitation Prior
title_fullStr Clustering Gene Expressions Using the Table Invitation Prior
title_full_unstemmed Clustering Gene Expressions Using the Table Invitation Prior
title_short Clustering Gene Expressions Using the Table Invitation Prior
title_sort clustering gene expressions using the table invitation prior
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9690110/
https://www.ncbi.nlm.nih.gov/pubmed/36360270
http://dx.doi.org/10.3390/genes13112036
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