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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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

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Related Experiment Video

Updated: Jun 22, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

VisHiC--hierarchical functional enrichment analysis of microarray data.

Darya Krushevskaya1, Hedi Peterson, Jüri Reimand

  • 1Institute of Computer Science, University of Tartu, Liivi 2, 50409 Tartu, Estonian.

Nucleic Acids Research
|June 2, 2009
PubMed
Summary
This summary is machine-generated.

VisHiC is a new web server that clusters gene expression data from microarrays. It visualizes results by highlighting biologically relevant gene clusters based on Gene Ontology (GO) analysis, simplifying complex genomic data.

Related Experiment Videos

Last Updated: Jun 22, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Microarray technology is crucial for measuring gene expression levels in genomics.
  • Clustering gene expression data helps identify genes in common biological pathways and functions.
  • Gene Ontology (GO) analysis and visualization are essential for understanding the biological context of gene clusters.

Purpose of the Study:

  • To present VisHiC, a web server for clustering and visualizing gene expression data.
  • To integrate automated function enrichment analysis with data visualization.
  • To provide a compact and informative overview of large gene expression datasets.

Main Methods:

  • Hierarchical clustering of gene expression matrices.
  • Automated Gene Ontology (GO) term, pathway, and regulatory motif enrichment analysis.
  • Compact visualization of results using dendrograms and heatmaps, contracting enriched clusters.

Main Results:

  • VisHiC generates a dendrogram and visual heatmap highlighting biologically relevant clusters.
  • Enrichment analysis identifies significant GO terms, pathways, and regulatory motifs within clusters.
  • The visualization emphasizes significant clusters while hiding less relevant data for a condensed overview.

Conclusions:

  • VisHiC offers an efficient method for analyzing and visualizing complex gene expression data.
  • The tool aids researchers in identifying biologically meaningful gene clusters and understanding their functions.
  • VisHiC provides a valuable starting point for further in-depth analysis of genomic data.