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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Jun 23, 2026

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

Published on: October 3, 2025

A practical multifaceted approach to selecting differentially expressed genes.

Yingye Zheng1, Margaret Pepe

  • 1Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. yzheng@fhcrc.org

Cancer Informatics
|May 21, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces the mp-value, a novel measure for quantifying evidence of differential gene expression in microarray studies. It enhances gene selection by considering expression extent and evidence strength alongside error rates.

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Genomics

Background:

  • Gene expression array studies aim to identify differentially expressed genes.
  • Traditional methods focus on controlling error rates like false discovery rate (FDR) and family-wise error rate (FWER).
  • These methods may overlook other crucial aspects of gene selection, such as the magnitude and strength of evidence for differential expression.

Purpose of the Study:

  • To demonstrate the importance of evaluating the extent and strength of differential gene expression in exploratory analysis.
  • To introduce a new statistical measure, the mp-value, for quantifying the strength of evidence for differential expression.
  • To compare the utility of mp-values with traditional multiple testing p-values.

Main Methods:

  • Utilized real and simulated gene expression array data.
  • Developed a resampling-based algorithm to calculate mp-values, accounting for multiplicity and dependence in microarray data.
  • Contrasted mp-values with multiple testing p-values using a breast cancer prognosis dataset and simulation models.

Main Results:

  • Demonstrated that exploratory analysis should incorporate measures of differential expression extent and evidence strength, in addition to error rate control.
  • The proposed mp-value quantifies the strength of evidence for differential expression.
  • Mp-values are descriptive and do not rely on predefined decision rules, unlike traditional p-values.

Conclusions:

  • The mp-value offers a valuable, descriptive measure for assessing the strength of evidence in differential gene expression analysis.
  • Integrating mp-values into the analysis pipeline can lead to more robust gene selection in microarray studies.
  • This approach complements traditional error rate control methods for comprehensive gene discovery.