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Related Experiment Videos

Combining transcriptional datasets using the generalized singular value decomposition.

Andreas W Schreiber1, Neil J Shirley, Rachel A Burton

  • 1Australian Centre for Plant Functional Genomics, School of Agriculture and Wine, University of Adelaide, Waite Campus, Glen Osmond, SA 5064, Australia. andreas.schreiber@adelaide.edu.au

BMC Bioinformatics
|August 9, 2008
PubMed
Summary
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Generalized singular value decomposition merges gene expression datasets from microarrays and quantitative real-time PCR. This method enables co-expression analysis across datasets with partial overlap, aiding in discovering novel genes.

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Microarrays and quantitative real-time PCR (qRT-PCR) are key for gene expression studies.
  • Platform-specific effects hinder direct comparison of datasets from different technologies.
  • Partial overlap in genes and experimental conditions complicates data integration.

Purpose of the Study:

  • To develop a method for merging gene expression datasets from microarrays and qRT-PCR.
  • To enable co-expression analysis between datasets with dissimilar gene sets and conditions.
  • To facilitate the discovery of novel genes involved in specific biological pathways.

Main Methods:

  • Generalized singular value decomposition (GSVD) was applied to integrate datasets.
  • GSVD was optimized using selected genes for robust analysis.

Related Experiment Videos

  • The method was tested for its ability to identify co-expressed genes across datasets.

    • Main Results:

    • GSVD successfully merged microarray and qRT-PCR datasets, even with partial overlap.
    • The technique identified genes co-expressed across datasets with differing experimental conditions.
    • Candidate genes involved in (1,3;1,4)-beta-D-glucan biosynthesis were discovered.

    Conclusions:

    • GSVD offers a viable approach for combined analysis of heterogeneous gene expression data.
    • The method allows for seamless definition of co-expression across datasets.
    • This technique is valuable for leveraging public microarray data with in-house measurements, especially for unsequenced genomes.