Dataset Information

Empirical null distribution based modeling of multi-class differential gene expression detection.

ABSTRACT: In this paper, we study the multi-class differential gene expression detection for microarray data. We propose a likelihood based approach to estimating an empirical null distribution to incorporate gene interactions and provide more accurate false positive control than the commonly used permutation or theoretical null distribution based approach. We propose to rank important genes by p-values or local false discovery rate based on the estimated empirical null distribution. Through simulations and application to a lung transplant microarray data, we illustrate the competitive performance of the proposed method.

SUBMITTER: Cao X

PROVIDER: S-EPMC3607635 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Publications

Empirical null distribution based modeling of multi-class differential gene expression detection.

Cao Xiting X Wu Baolin B Hertz Marshall I MI

Journal of applied statistics 20121121 2

In this paper, we study the multi-class differential gene expression detection for microarray data. We propose a likelihood based approach to estimating an empirical null distribution to incorporate gene interactions and provide more accurate false positive control than the commonly used permutation or theoretical null distribution based approach. We propose to rank important genes by p-values or local false discovery rate based on the estimated empirical null distribution. Through simulations a ...[more]

PMID: 23538964

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Dataset Information

Empirical null distribution based modeling of multi-class differential gene expression detection.

Publications

Empirical null distribution based modeling of multi-class differential gene expression detection.

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