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DNA Microarrays

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: May 6, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

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Published on: June 27, 2020

A Protocol for Weighted Gene Co-expression Network Analysis With Module Preservation and Functional Enrichment

Phuong Nguyen^1,2, Erliang Zeng^1,2,3

¹Division of Biostatistics and Computational Biology, College of Dentistry and Dental Clinics, University of Iowa, Iowa City, IA, USA.

|September 26, 2025

Summary

This summary is machine-generated.

This study introduces a Weighted Gene Co-expression Network Analysis (WGCNA) protocol for comparing paired tumor and normal gene expression data. It identifies conserved and disrupted gene modules, enhancing understanding of cancer pathogenesis and universal biological processes.

Keywords:

Functional enrichment analysis Gene expression Module preservation analysis Oral cancer WGCNA

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Published on: August 25, 2021

Area of Science:

Transcriptomics
Bioinformatics
Systems Biology

Background:

Weighted Gene Co-expression Network Analysis (WGCNA) is crucial for identifying gene modules in transcriptomic studies.
Existing WGCNA protocols often lack methods for comparing module stability across different conditions.
Understanding gene expression changes in paired tumor and normal tissues is vital for disease mechanism research.

Purpose of the Study:

To present a comprehensive protocol for constructing and comparing WGCNA modules in paired tumor and normal datasets.
To enable the identification of gene modules involved in both fundamental biological processes and cancer-specific pathogenesis.
To provide a framework for module preservation analysis in paired data for deeper insights into disease molecular underpinnings.

Main Methods:

Developed a step-by-step Weighted Gene Co-expression Network Analysis (WGCNA) protocol.
Incorporated module preservation and functional enrichment analysis using TCGA cancer data.
Preprocessed gene expression data and performed downstream network analysis for paired normal and tumor tissues.

Main Results:

Demonstrated network differences between normal and tumor tissues using the WGCNA protocol.
Enabled identification of conserved and disrupted gene co-expression modules across conditions.
Provided a framework for analyzing module stability and functional relevance in paired datasets.

Conclusions:

The presented WGCNA protocol facilitates the comparison of gene modules in paired datasets, distinguishing core biological processes from disease-specific alterations.
This approach enhances the understanding of oral cancer and other diseases by revealing conserved and disrupted molecular networks.
The protocol offers a valuable tool for researchers investigating gene co-expression patterns and their functional implications in various biological contexts.