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Related Experiment Video

Updated: Oct 14, 2025

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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T2-DAG: a powerful test for differentially expressed gene pathways via graph-informed structural equation modeling.

Jin Jin1, Yue Wang2

  • 1Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.

Bioinformatics (Oxford, England)
|November 10, 2021
PubMed
Summary

We developed T2-DAG, a novel statistical test for identifying differentially expressed gene pathways. This method significantly improves power by utilizing gene interaction data, aiding disease diagnosis and understanding genetic mutations.

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical genomics
  • Computational biology

Background:

  • Identifying genes and pathways linked to human diseases and traits is crucial for diagnosis and understanding genetic mutations.
  • Gene pathway analysis offers more biologically relevant insights than individual gene analysis but faces challenges in high-dimensional two-sample testing with limited data.
  • Existing methods often underutilize auxiliary pathway information, leading to reduced statistical power in detecting differentially expressed pathways.

Purpose of the Study:

  • To propose T2-DAG, a novel Hotelling's T2-type test designed for detecting differentially expressed gene pathways.
  • To efficiently incorporate auxiliary pathway information on gene interactions using a linear structural equation model.
  • To evaluate the performance of T2-DAG against existing methods in terms of statistical power and error rates.

Main Methods:

  • Developed T2-DAG, a Hotelling's T2-type statistical test for high-dimensional two-sample pathway analysis.
  • Integrated gene interaction data from pathway databases via a linear structural equation model.
  • Established the asymptotic distribution of the T2-DAG test under relevant statistical assumptions.

Main Results:

  • T2-DAG demonstrated superior statistical power compared to several existing methods across various simulation scenarios.
  • The test maintained well-controlled type-I error rates, even with incomplete or inaccurate pathway information and unadjusted confounding effects.
  • Applied T2-DAG to identify differentially expressed KEGG pathways between different stages of lung cancer, showcasing practical utility.

Conclusions:

  • T2-DAG effectively leverages gene interaction information to enhance the detection of differentially expressed gene pathways.
  • The proposed method offers a powerful and robust approach for genetic studies, improving upon existing two-sample testing strategies.
  • The T2DAG R package is available for broader application in genetic research and clinical diagnostics.