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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: Apr 8, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data.

David Gutiérrez-Avilés¹, Cristina Rubio-Escudero¹

¹Department of Computer Science, University of Seville, Seville, Spain.

Evolutionary Bioinformatics Online

|July 1, 2015

Summary

This study introduces the Multi Slope Measure, a novel evaluation metric for triclustering gene expression data. This method enhances the analysis of temporal experiments, revealing complex biological patterns in gene behavior over time and conditions.

Keywords:

angular comparison fitness function genetic algorithms microarrays time series triclustering

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray technology is crucial for monitoring RNA levels in biological research.
Analyzing microarray data presents computational challenges due to data complexity.
Clustering, biclustering, and triclustering are methods for grouping genes with similar behaviors.

Purpose of the Study:

To introduce a new evaluation measure for triclusters.
To facilitate the analysis of longitudinal experiments using microarray data.
To uncover hidden biological patterns in gene expression over time and conditions.

Main Methods:

Development of the Multi Slope Measure (MSM).
MSM evaluates triclusters based on the similarity of slope angles within gene profiles.
Application to temporal microarray experiments.

Main Results:

The Multi Slope Measure quantifies the quality of triclusters.
It captures patterns relating subsets of genes, conditions, and time points.
Provides a robust method for analyzing complex temporal gene expression data.

Conclusions:

The Multi Slope Measure offers a novel approach to evaluating triclusters in temporal gene expression analysis.
This metric aids in discovering intricate biological insights from longitudinal microarray studies.
Enhances the understanding of gene behavior patterns across genes, conditions, and time.