Startseite A Bayesian model to identify multiple expression patterns with simultaneous FDR control for a multi-factor RNA-seq experiment
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A Bayesian model to identify multiple expression patterns with simultaneous FDR control for a multi-factor RNA-seq experiment

  • Yuanyuan Bian , Chong He und Jing Qiu EMAIL logo
Veröffentlicht/Copyright: 24. April 2023
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Statistical Applications in Genetics and Molecular Biology
Aus der Zeitschrift Statistical Applications in Genetics and Molecular Biology Band 22 Heft 1

Abstract

It is often of research interest to identify genes that satisfy a particular expression pattern across different conditions such as tissues, genotypes, etc. One common practice is to perform differential expression analysis for each condition separately and then take the intersection of differentially expressed (DE) genes or non-DE genes under each condition to obtain genes that satisfy a particular pattern. Such a method can lead to many false positives, especially when the desired gene expression pattern involves equivalent expression under one condition. In this paper, we apply a Bayesian partition model to identify genes of all desired patterns while simultaneously controlling their false discovery rates (FDRs). Our simulation studies show that the common practice fails to control group specific FDRs for patterns involving equivalent expression while the proposed Bayesian method simultaneously controls group specific FDRs at all settings studied. In addition, the proposed method is more powerful when the FDR of the common practice is under control for identifying patterns only involving DE genes. Our simulation studies also show that it is an inherently more challenging problem to identify patterns involving equivalent expression than patterns only involving differential expression. Therefore, larger sample sizes are required to obtain the same target power to identify the former types of patterns than the latter types of patterns.

Keywords: Bayesian model; equivalent expression; group specific FDR; multiple gene expression patterns; nonlocal prior
Corresponding author: Jing Qiu, Department of Applied Economics and Statistics, University of Delaware, Newark, DE, USA, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work was partially done with the use of the BIOMIX compute cluster at University of Delaware, which was made possible through funding from Delaware INBRE (NIGMS P20GM103446), the State of Delaware and the Delaware Biotechnology Institute.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/sagmb-2022-0025).

Received: 2022-05-10
Accepted: 2023-02-13
Published Online: 2023-04-24

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/sagmb-2022-0025
Schlagwörter für diesen Artikel
Bayesian model; equivalent expression; group specific FDR; multiple gene expression patterns; nonlocal prior

Artikel in diesem Heft

  1. Review
  2. Mediation analysis method review of high throughput data
  3. Research Articles
  4. When is the allele-sharing dissimilarity between two populations exceeded by the allele-sharing dissimilarity of a population with itself?
  5. Patterns of differential expression by association in omic data using a new measure based on ensemble learning
  6. Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization
  7. Randomized singular value decomposition for integrative subtype analysis of ‘omics data’ using non-negative matrix factorization
  8. A novel hybrid CNN and BiGRU-Attention based deep learning model for protein function prediction
  9. Accurate and fast small p-value estimation for permutation tests in high-throughput genomic data analysis with the cross-entropy method
  10. Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets
  11. A Bayesian model to identify multiple expression patterns with simultaneous FDR control for a multi-factor RNA-seq experiment
  12. A fast and efficient approach for gene-based association studies of ordinal phenotypes
  13. Software and Application Note
  14. CAT PETR: a graphical user interface for differential analysis of phosphorylation and expression data
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