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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Clustering approaches to identifying gene expression patterns from DNA microarray data.

Jin Hwan Do1, Dong-Kug Choi

  • 1Department of Biotechnology, Konkuk University, Chungju 380-701, Korea.

Molecules and Cells
|April 17, 2008
PubMed
Summary

This review explores clustering techniques for analyzing gene expression microarray data. Understanding these methods is crucial for biologists to interpret results accurately and uncover biological insights.

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
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Published on: March 15, 2011

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray data analysis is vital for understanding gene expression patterns.
  • Co-expressed genes often share regulatory mechanisms, aiding in functional annotation and pathway elucidation.
  • Clustering techniques are commonly used to identify co-expressed genes in microarray experiments.

Purpose of the Study:

  • To review and survey clustering techniques for DNA microarray data analysis.
  • To highlight the importance of understanding clustering algorithm strengths and weaknesses for biologists.
  • To provide an overview of basic and complex clustering approaches.

Main Methods:

  • Survey of various clustering algorithms applied to gene expression data.
  • Discussion of crisp clustering methods: hierarchical clustering, K-means, and self-organizing maps.
  • Exploration of complex clustering algorithms, including fuzzy clustering.

Main Results:

  • Clustering results can vary significantly based on algorithms, metrics, and user-defined parameters.
  • Awareness of method-specific limitations is essential for accurate biological interpretation.
  • Different clustering approaches offer diverse ways to group and analyze gene expression patterns.

Conclusions:

  • Effective interpretation of microarray data relies on a solid understanding of clustering methodologies.
  • Choosing appropriate clustering techniques and parameters is critical for reliable biological discovery.
  • This review provides a foundational understanding of clustering for gene expression analysis.