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Singular value decomposition for genome-wide expression data processing and modeling.

O Alter1, P O Brown, D Botstein

  • 1Departments of Genetics and Biochemistry, Stanford University, Stanford, CA 94305, USA. orly@genome.stanford.edu

Proceedings of the National Academy of Sciences of the United States of America
|August 30, 2000
PubMed
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Singular value decomposition transforms gene expression data into eigengenes and eigenarrays. This method normalizes data by removing noise, enabling comparisons and revealing patterns in gene regulation and cellular states.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Genome-wide expression data analysis is complex.
  • Identifying meaningful biological signals from experimental noise is challenging.

Purpose of the Study:

  • To introduce a novel method for transforming and analyzing genome-wide expression data.
  • To enable robust comparisons of gene expression across different experimental conditions.

Main Methods:

  • Application of singular value decomposition (SVD) to gene expression matrices.
  • Transformation of gene x array data into eigengenes x eigenarrays space.
  • Normalization by filtering noise-associated eigengenes/eigenarrays.

Main Results:

Related Experiment Videos

  • SVD effectively reduces data dimensionality while preserving biological information.
  • Normalization enhances the comparability of gene expression data.
  • Data sorting reveals clusters of genes with similar regulation and arrays with similar phenotypes.
  • Significant eigengenes/eigenarrays correlate with regulator activity.

    • Conclusions:

    • SVD provides a powerful framework for analyzing complex genomic datasets.
    • The eigengene/eigenarray approach facilitates noise reduction and biological interpretation.
    • This method aids in understanding gene regulatory networks and cellular states.