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Related Experiment Videos

Clustering microarray data.

Jeremy Gollub1, Gavin Sherlock

  • 1Department of Biochemistry, Stanford University Medical School, Stanford, CA, USA.

Methods in Enzymology
|August 31, 2006
PubMed
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Microarray experiments yield massive datasets. This chapter details hierarchical clustering and partitioning methods for organizing this biological data, along with available software tools.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray experiments generate vast amounts of data, often millions of data points.
  • Traditional data analysis tools like spreadsheets are inadequate for managing such large datasets.
  • Systematic methods are essential for effective selection and organization of microarray data.

Purpose of the Study:

  • To introduce the concepts and algorithms behind hierarchical clustering.
  • To explain common methods for partitioning and organizing microarray data.
  • To highlight freely available software for implementing these data analysis techniques.

Main Methods:

  • Focuses on hierarchical clustering algorithms.
  • Explains partitioning methods for data organization.

Related Experiment Videos

  • Reviews freely available bioinformatics software.

    • Main Results:

    • Provides a framework for understanding hierarchical clustering in genomics.
    • Outlines practical approaches for microarray data organization.
    • Identifies accessible software solutions for researchers.

    Conclusions:

    • Hierarchical clustering and partitioning are crucial for analyzing large-scale microarray data.
    • Freely available software simplifies the implementation of these advanced analytical methods.
    • Effective data organization is key to extracting meaningful biological insights from high-throughput experiments.