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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: Nov 17, 2025

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

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

Published on: June 27, 2020

A guide to creating design matrices for gene expression experiments.

Charity W Law^1,2, Kathleen Zeglinski^1,3, Xueyi Dong^1,2

¹The Walter and Eliza Hall Institute of Medical Research, Parkville, 3052, Australia.

|February 19, 2021

Summary

This summary is machine-generated.

This guide simplifies setting up design and contrast matrices for differential gene expression analysis, crucial for RNA-sequencing studies. It provides practical examples for various linear models, aiding researchers in genomic data analysis.

Keywords:

Design matrix contrast matrix gene expression analysis model matrix statistical models

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Differential expression analysis of genomic data, like RNA-sequencing, relies on linear models to identify gene expression changes.
Setting up appropriate design and contrast matrices is a common challenge in these analyses due to a lack of comprehensive resources.

Purpose of the Study:

To provide a practical guide for constructing design and contrast matrices for differential expression analysis.
To demystify the process of setting up linear models for genomic data analysis.

Main Methods:

The article offers a practical approach with code and graphical representations for setting up design and contrast matrices.
It covers a range of models, from simple single-variable models to complex interaction, mixed-effects, time-series, and cyclical models.

Main Results:

The guide facilitates the understanding and implementation of design and contrast matrices for various linear models.
It provides clear explanations and examples applicable to limma-style pipelines and adaptable to other software and high-throughput technologies.

Conclusions:

This resource aims to make differential expression analysis more accessible to researchers of all levels.
By clarifying model setup, it empowers scientists to make informed choices for their genomic data analysis.