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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...
RNA-seq03:21

RNA-seq

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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Cluster Sampling Method01:20

Cluster Sampling Method

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Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

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Related Experiment Video

Updated: Jun 6, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Merged consensus clustering to assess and improve class discovery with microarray data.

T Ian Simpson1, J Douglas Armstrong, Andrew P Jarman

  • 1Genes and Development Group, Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh, EH8 9XD, UK. ian.simpson@ed.ac.uk

BMC Bioinformatics
|December 7, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an R package for robust gene expression data clustering. It enhances cluster analysis by merging results from different methods, improving reliability and identifying the best-supported clusters.

Related Experiment Videos

Last Updated: Jun 6, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput gene expression data analysis commonly employs clustering to classify data into groups.
  • Numerous clustering methods exist, but selecting the most appropriate and quantifying classification quality remains challenging.

Purpose of the Study:

  • To introduce an R package for analyzing the consistency of clustering results using resampling statistics.
  • To enable identification of well-supported clusters and rank cluster members by fidelity.
  • To facilitate comparison of different clustering algorithms and selection of reliable methods.

Main Methods:

  • Utilizes resampling statistics to analyze clustering result consistency.
  • Implements a merged consensus clustering methodology within the R programming environment.
  • Provides analysis and graphical display functions for exploring clustering approaches.

Main Results:

  • Identifies best-supported clusters and ranks cluster members by fidelity.
  • Enables comparison of clustering algorithm performance under various conditions.
  • Demonstrates application to simulated data, gene expression experiments, and fruitfly nervous system development genes.

Conclusions:

  • The R package, clusterCons, offers a convenient way to apply merged consensus clustering.
  • Enhances consensus clustering by merging results from different methods for averaged robustness.
  • Effectively corrects for outliers and provides reliable clustering structures.