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<p>A commonly used approach for estimating the number of genes that are DE in multiple experiments is to analyze the data from each experiment separately, generate a list of DE genes, and then count genes that appear in all lists (i.e., intersection method). The intersection method heavily depends on false discovery rate (FDR)-level control. The drawback of the intersection method is its high dependency on the FDR level. In this research, we proposed a Uniform–Beta mixture model for the purpose of estimating the number of genes commonly DE in multiple independent experiments. The proposed Uniform–Beta mixture model uses p values with assumption that these p values are uniformly distributed for equivalently expressed (EE) genes and are assumed to follow a right-skewed beta distribution for DE genes. Supplementary materials accompanying this paper appear on-line.</p>

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An Improved Approach to Estimating Genes Commonly Differentially Expressed In Multiple Two-Sample Experiments

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Jerry Dogbey-Gakpetor,
  • Megan Orr

摘要

A commonly used approach for estimating the number of genes that are DE in multiple experiments is to analyze the data from each experiment separately, generate a list of DE genes, and then count genes that appear in all lists (i.e., intersection method). The intersection method heavily depends on false discovery rate (FDR)-level control. The drawback of the intersection method is its high dependency on the FDR level. In this research, we proposed a Uniform–Beta mixture model for the purpose of estimating the number of genes commonly DE in multiple independent experiments. The proposed Uniform–Beta mixture model uses p values with assumption that these p values are uniformly distributed for equivalently expressed (EE) genes and are assumed to follow a right-skewed beta distribution for DE genes. Supplementary materials accompanying this paper appear on-line.