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Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data
Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649281/ https://www.ncbi.nlm.nih.gov/pubmed/23690879 http://dx.doi.org/10.1155/2013/693901 |
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author | Mori, Keita Oura, Tomonori Noma, Hisashi Matsui, Shigeyuki |
author_facet | Mori, Keita Oura, Tomonori Noma, Hisashi Matsui, Shigeyuki |
author_sort | Mori, Keita |
collection | PubMed |
description | Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided. |
format | Online Article Text |
id | pubmed-3649281 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-36492812013-05-20 Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data Mori, Keita Oura, Tomonori Noma, Hisashi Matsui, Shigeyuki Comput Math Methods Med Research Article Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided. Hindawi Publishing Corporation 2013 2013-04-10 /pmc/articles/PMC3649281/ /pubmed/23690879 http://dx.doi.org/10.1155/2013/693901 Text en Copyright © 2013 Keita Mori et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Mori, Keita Oura, Tomonori Noma, Hisashi Matsui, Shigeyuki Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data |
title | Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data |
title_full | Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data |
title_fullStr | Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data |
title_full_unstemmed | Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data |
title_short | Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data |
title_sort | cancer outlier analysis based on mixture modeling of gene expression data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649281/ https://www.ncbi.nlm.nih.gov/pubmed/23690879 http://dx.doi.org/10.1155/2013/693901 |
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