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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...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Comparing Copy Number Variations and SNPs02:26

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What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...
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What is Gene Expression?

Overview
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Related Experiment Video

Updated: Jul 4, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A ground truth based comparative study on clustering of gene expression data.

Yitan Zhu1, Zuyi Wang, David J Miller

  • 1Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA.

Frontiers in Bioscience : a Journal and Virtual Library
|May 30, 2008
PubMed
Summary

This study validates gene expression clustering algorithms. VISDA performed well, while K-means and self-organizing maps offered stable solutions for gene expression data analysis.

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Last Updated: Jul 4, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data analysis relies on various clustering methods.
  • Rigorous validation is crucial to assess algorithm performance and limitations.

Purpose of the Study:

  • To comparatively evaluate five clustering algorithms: hierarchical clustering, K-means, self-organizing maps, finite normal mixture fitting, and VISDA.
  • To assess functionality, accuracy, and stability using gene expression datasets.

Main Methods:

  • Ground truth-based comparative study.
  • Testing on seven published microarray gene expression datasets and one synthetic dataset.
  • Evaluation in data-sufficient and data-insufficient scenarios using quantitative measures like cluster number detection and partition accuracy.

Main Results:

  • VISDA (VIsual Statistical Data Analyzer) demonstrated solid performance across most datasets.
  • K-means clustering and self-organizing maps yielded more stable results when optimized by mean squared compactness.
  • Performance was assessed using cluster number detection accuracy and partition accuracy metrics.

Conclusions:

  • VISDA is a reliable algorithm for gene expression clustering.
  • K-means and self-organizing maps provide stable clustering solutions, particularly under specific optimization criteria.
  • Algorithm choice impacts the reliability and stability of gene expression data analysis.