Dataset Information

Cancer outlier detection based on likelihood ratio test.

ABSTRACT:

Motivation

Microarray experiments can be used to help study the role of chromosomal translocation in cancer development through cancer outlier detection. The aim is to identify genes that are up- or down-regulated in a subset of cancer samples in comparison to normal samples.

Results

We propose a likelihood-based approach which targets detecting the change of point in mean expression intensity in the group of cancer samples. A desirable property of the proposed approach is the availability of theoretical significance-level results. Simulation studies showed that the performance of the proposed approach is appealing in terms of both detection power and false discovery rate. And the real data example also favored the likelihood-based approach in terms of the biological relevance of the results.

Availability

R code to implement the proposed method in the statistical package R is available at: http://odin.mdacc.tmc.edu/~jhhu/cod-analysis/.

SUBMITTER: Hu J

PROVIDER: S-EPMC2732272 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Publications

Cancer outlier detection based on likelihood ratio test.

Hu Jianhua J

Bioinformatics (Oxford, England) 20080812 19

<h4>Motivation</h4>Microarray experiments can be used to help study the role of chromosomal translocation in cancer development through cancer outlier detection. The aim is to identify genes that are up- or down-regulated in a subset of cancer samples in comparison to normal samples.<h4>Results</h4>We propose a likelihood-based approach which targets detecting the change of point in mean expression intensity in the group of cancer samples. A desirable property of the proposed approach is the ava ...[more]

PMID: 18697774

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Project description:The assumption of Hardy-Weinberg equilibrium (HWE) is generally required for association analysis using case-control design on autosomes; otherwise, the size may be inflated. There has been an increasing interest of exploring the association between diseases and markers on X chromosome and the effect of the departure from HWE on association analysis on X chromosome. Note that there are two hypotheses of interest regarding the X chromosome: (i) the frequencies of the same allele at a locus in males and females are equal and (ii) the inbreeding coefficient in females is zero (without excess homozygosity). Thus, excess homozygosity and significantly different minor allele frequencies between males and females are used to filter X-linked variants. There are two existing methods to test for (i) and (ii), respectively. However, their size and powers have not been studied yet. Further, there is no existing method to simultaneously detect both hypotheses till now. Therefore, in this article, we propose a novel likelihood ratio test for both (i) and (ii) on X chromosome. To further investigate the underlying reason why the null hypothesis is statistically rejected, we also develop two likelihood ratio tests for detecting (i) and (ii), respectively. Moreover, we explore the effect of population stratification on the proposed tests. From our simulation study, the size of the test for (i) is close to the nominal significance level. However, the size of the excess homozygosity test and the test for both (i) and (ii) is conservative. So, we propose parametric bootstrap techniques to evaluate their validity and performance. Simulation results show that the proposed methods with bootstrap techniques control the size well under the respective null hypothesis. Power comparison demonstrates that the methods with bootstrap techniques are more powerful than those without bootstrap procedure and the existing methods. The application of the proposed methods to a rheumatoid arthritis dataset indicates their utility.

Dataset Information

Cancer outlier detection based on likelihood ratio test.

Motivation

Results

Availability

Publications

Cancer outlier detection based on likelihood ratio test.

Similar Datasets

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