Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data
Joint Authors
Noma, Hisashi
Matsui, Shigeyuki
Mori, Keita
Oura, Tomonori
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-04-10
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Mori, Keita& Oura, Tomonori& Noma, Hisashi& Matsui, Shigeyuki. 2013. Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-491138
Modern Language Association (MLA)
Mori, Keita…[et al.]. Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-491138
American Medical Association (AMA)
Mori, Keita& Oura, Tomonori& Noma, Hisashi& Matsui, Shigeyuki. Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-491138
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-491138