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

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. Results: We have examined the effect of discrete data selection criteria (qualification criteria for inclusion) and response thresholds (out-put filtering) on the number of significant genes reported by SAM. The use of a reduced data set by applying arbitrary restrictions vis-

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Ola Larsson, Claes Wahlestedt, James A. Timmons
• Keywords: BMCBI 2005, Data Analysis, Significant Expression Changes, Significant Gene