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Related Experiment Videos

Considerations when using the significance analysis of microarrays (SAM) algorithm.

Ola Larsson1, Claes Wahlestedt, James A Timmons

  • 1Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius Väg, 35, 171 77 Stockholm, Sweden. ola.larsson@cgb.ki.se

BMC Bioinformatics
|June 1, 2005
PubMed
Summary
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Thresholds in Significance Analysis of Microarrays (SAM) can significantly alter gene expression study results. Researchers must carefully consider data selection and filtering criteria to avoid biased conclusions in gene expression analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Gene Expression Analysis

Background:

  • Microarray technology is widely used for gene expression studies.
  • Significance Analysis of Microarrays (SAM) is a common analysis method.
  • The impact of data selection thresholds on SAM results is not well-documented.

Purpose of the Study:

  • To evaluate how data selection criteria and output thresholds affect SAM results.
  • To investigate the influence of fold change (FC) hurdles on SAM output.
  • To assess the impact of these alterations on downstream analyses.

Main Methods:

  • Examined the effect of data qualification and response thresholds on SAM.
  • Analyzed alterations in significant gene lists using different data restrictions.

Related Experiment Videos

  • Investigated the impact of the FC hurdle in Microsoft Excel's SAM implementation.

    • Main Results:

    • Data selection criteria and FC hurdles substantially alter significant gene lists in SAM.
    • The FC hurdle modifies the control dataset, changing significance levels (q-values).
    • These changes can significantly impact post hoc analyses like ontology overrepresentation.

    Conclusions:

    • Caution is advised when using SAM due to threshold effects.
    • Reanalysis of datasets considering arbitrary threshold impacts is recommended.
    • Ensuring robust gene expression data interpretation requires careful threshold evaluation.