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Large-scale analysis of gene expression profiles.

Thomas D Wu1

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

Briefings in Bioinformatics
|May 11, 2002
PubMed
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Gene expression profiles from DNA microarray data enable analysis of gene expression. Grouping samples reveals insights into tissues and diseases, aiding candidate gene identification through hypothesis testing.

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • DNA microarray data accumulation enables gene expression analysis.
  • Gene expression profiles offer insights into gene behavior across samples.
  • Expression signatures provide data for a single sample.

Purpose of the Study:

  • To analyze gene expression data using gene expression profiles.
  • To contrast expression profiles with expression signatures.
  • To explore the utility of expression profiles in understanding biological samples.

Main Methods:

  • Utilizing DNA microarray data.
  • Analyzing gene expression profiles.
  • Grouping samples by clinical, pathological, or cluster analysis categories.
  • Applying hypothesis tests to expression profiles.

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Main Results:

  • Gene expression profiles reveal patterns across various samples.
  • Appropriate sample grouping enhances the interpretability of expression profiles.
  • Expression profiles provide information across different tissues and diseases.
  • Large-scale hypothesis testing identifies candidate genes.

Conclusions:

  • Gene expression profiles are valuable tools for analyzing microarray data.
  • Effective sample stratification is crucial for maximizing the insights gained from expression profiles.
  • Expression profile analysis facilitates the identification of genes relevant to specific conditions or tissues.