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Related Experiment Videos

Analysing gene expression data from DNA microarrays to identify candidate genes.

T D Wu1

  • 1Department of Bioinformatics, Genentech, Inc., South San Francisco, CA 94080, USA. twu@gene.com

The Journal of Pathology
|September 25, 2001
PubMed
Summary

This review covers gene filtering methods in microarray data analysis, focusing on hypothesis testing for identifying specific genes. It examines analytical approaches for diverse experimental designs, aiding biological research.

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Bioinformatics (Oxford, England)·2000

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Analysis

Background:

  • Microarray data analysis involves gene grouping and filtering.
  • Gene grouping uses cluster analysis, while gene filtering relies on hypothesis testing.

Purpose of the Study:

  • To review analytical methods for the gene-filtering task in microarray data analysis.
  • To discuss data analysis for various experimental protocols.

Main Methods:

  • Survey of analytical methods for gene filtering.
  • Discussion of data analysis for four basic experimental protocols: two samples, two conditions with replicates, multiple conditions, and covariate analysis.

Main Results:

  • Identifies and categorizes various statistical approaches for gene filtering.

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  • Provides a framework for selecting appropriate methods based on experimental design.

    • Conclusions:

    • Hypothesis testing is central to gene filtering in microarray studies.
    • Understanding different analytical methods is crucial for accurate gene identification and biological interpretation.