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Related Experiment Video

Updated: Oct 27, 2025

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

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DysPIA: A Novel Dysregulated Pathway Identification Analysis Method.

Limei Wang1,2,3, Weixin Xie1, Kongning Li2

  • 1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.

Frontiers in Genetics
|July 22, 2021
PubMed
Summary
This summary is machine-generated.

Differential co-expression analysis is enhanced by Dysregulated Pathway Identification Analysis (DysPIA). This novel method identifies causal pathways efficiently and accurately, outperforming existing approaches in speed and biological relevance.

Keywords:
differential co-expressiondifferential expressiondifferential variabilitydysregulated pathwayenrichment analysisgene regulation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Differential co-expression analysis is crucial for understanding biological pathways but current methods are limited.
  • Existing approaches often treat pathways as gene sets, neglecting gene regulation and exhibiting slow computational speeds.

Purpose of the Study:

  • To introduce Dysregulated Pathway Identification Analysis (DysPIA), a novel method for differential co-expression-based pathway analysis.
  • To address limitations of existing methods by incorporating gene regulation and improving computational efficiency.

Main Methods:

  • DysPIA utilizes the Correlation by Individual Level Product for fast enrichment analysis.
  • A comprehensive gene-pair background was constructed, improving upon previous methods like Edge Set Enrichment Analysis.
  • The method was validated using simulation studies, p53 mutation data, and breast cancer datasets.

Main Results:

  • DysPIA demonstrated high accuracy in identifying causal pathways in simulations (AUC 0.9584–0.9896).
  • The method outperformed existing approaches in analyzing p53 mutation data, identifying more literature-verifiable pathways.
  • DysPIA exhibited significantly faster computation speeds (1,700–8,000 times faster than alternatives).

Conclusions:

  • DysPIA is an effective and computationally efficient tool for differential co-expression-based pathway analysis.
  • The method holds significant biological relevance, as demonstrated by its application to cancer datasets.
  • R packages 'DysPIA' and 'DysPIAData' are available for public use on CRAN and GitHub.