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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9690110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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