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Adjusting for gene-specific covariates to improve RNA-seq analysis.

Hyeongseon Jeon1,2, Kyu-Sang Lim3, Yet Nguyen4

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.

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This study introduces a new method to control the positive false discovery rate (pFDR) for gene testing, considering gene-specific factors like length. The approach improves hypothesis testing by accounting for varying null probabilities.

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Area of Science:

  • Genomics
  • Statistical genetics
  • Bioinformatics

Background:

  • Traditional hypothesis testing in genomics often assumes homogeneity among tests.
  • Gene-specific covariates, such as gene length, can influence the probability of a null hypothesis being true.
  • Existing methods may not adequately address this heterogeneity, potentially leading to suboptimal statistical power.

Purpose of the Study:

  • To propose a novel positive false discovery rate (pFDR) controlling method for gene-specific hypothesis testing.
  • To develop a method that accounts for the dependence of the null probability on gene-specific covariate variables.
  • To provide a robust framework for selecting tuning parameters and estimating pFDR.

Main Methods:

  • Proposed a rejection rule incorporating two distinct null probabilities based on covariate dependence.
  • Developed a positive false discovery rate (pFDR) estimator using Storey's q-value framework.
  • Introduced a cross-validation procedure for tuning parameter selection to maximize significant hypotheses.

Main Results:

  • Simulation studies showed the proposed method performs comparably to or better than existing approaches.
  • The method effectively controls the positive false discovery rate (pFDR) in the presence of covariate-dependent null probabilities.
  • Data analysis confirmed the premise that null probabilities vary with gene-specific covariates.

Conclusions:

  • The novel pFDR controlling method offers an effective way to handle heterogeneity in gene-specific hypothesis testing.
  • Accounting for covariate-dependent null probabilities enhances the power of genomic data analysis.
  • The proposed method and its implementation provide a valuable tool for researchers in statistical genetics and genomics.