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Considerations when using the significance analysis of microarrays (SAM) algorithm
Authors:Ola?Larsson  author-information"  >  author-information__contact u-icon-before"  >  mailto:ola.larsson@cgb.ki.se"   title="  ola.larsson@cgb.ki.se"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author,Claes?Wahlestedt,James?A?Timmons  author-information"  >  author-information__contact u-icon-before"  >  mailto:jamie.timmons@fyfa.ki.se"   title="  jamie.timmons@fyfa.ki.se"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author
Affiliation:1.Center for Genomics and Bioinformatics,Karolinska Institutet,Stockholm,Sweden
Abstract:

Background  

Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments. One of the most frequently utilised methods for gene expression data analysis is SAM (significance analysis of microarrays). The impact of selection thresholds, on the output from SAM, may critically alter the conclusion of a study, yet this consideration has not been systematically evaluated in any publication.
Keywords:
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