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Related Concept Videos

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...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Related Experiment Video

Updated: Jun 6, 2026

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization
16:37

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

Spatial clustering of array CGH features in combination with hierarchical multiple testing.

Kyung In Kim1, Etienne Roquain, Mark A van de Wiel

  • 1National Cancer Institute, USA. kimki2@mail.nih.gov

Statistical Applications in Genetics and Molecular Biology
|December 4, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel clustering method for DNA copy number variations in tumor samples using array comparative genomic hybridization (aCGH) data. The approach effectively identifies genomic regions and their associations with clinical variables while controlling for errors.

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Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
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Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Related Experiment Videos

Last Updated: Jun 6, 2026

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization
16:37

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
09:16

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Array comparative genomic hybridization (aCGH) is crucial for analyzing DNA copy number alterations in cancer.
  • Existing methods may not fully capture spatial patterns or integrate region and cluster significance effectively.

Purpose of the Study:

  • To develop a robust clustering algorithm for DNA features in multi-sample tumor aCGH data.
  • To differentiate data-collapsing from true clustering based on spatial genomic dependency.
  • To establish a hierarchical multiple testing framework for region and cluster association with clinical variables.

Main Methods:

  • A model-based clustering algorithm employing a maximum likelihood principle to identify correlated genomic regions.
  • Cluster stability scores and cross-validation to assess the randomness and reliability of clustering results.
  • A hierarchical multiple testing procedure to control the Family-Wise Error Rate (FWER) for region and cluster significance.

Main Results:

  • The proposed clustering method effectively identifies spatial genomic dependencies, distinguishing them from coincidental associations.
  • The hierarchical testing approach allows simultaneous interpretation of region and cluster significance.
  • Validation on two cancer datasets demonstrates the practical utility of the developed procedures.

Conclusions:

  • The novel clustering approach provides a powerful tool for analyzing complex aCGH data in cancer genomics.
  • The integrated region and cluster testing framework offers a statistically sound method for identifying clinically relevant genomic alterations.
  • This methodology enhances the understanding of genomic instability in tumors and its clinical implications.