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| Content Provider | PubMed Central |
|---|---|
| Author | Mori, Keita Oura, Tomonori Noma, Hisashi Matsui, Shigeyuki |
| Copyright Year | 2013 |
| Abstract | 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. |
| Related Links | http://dx.doi.org/10.1155/2013/693901 |
| Starting Page | 693901 |
| File Format | |
| ISSN | 1748670X |
| e-ISSN | 17486718 |
| Journal | Computational and Mathematical Methods in Medicine |
| Volume Number | 2013 |
| Language | English |
| Publisher | Hindawi Publishing Corporation |
| Publisher Date | 2013-01-01 |
| Access Restriction | Open |
| Rights Holder | Hindawi Publishing Corporation |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Modelling and Simulation Immunology and Microbiology(all) Applied Mathematics Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology Modeling and Simulation |
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