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Significance analysis of microarrays (SAM) is a statistical technique, established in 2001 by Virginia Tusher, Robert Tibshirani and Gilbert Chu, for determining whether changes in gene expression are statistically significant. With the advent of DNA microarrays, it is now possible to measure the expression of thousands of genes in a single hybridization experiment. The data generated is considerable, and a method for sorting out what is significant and what isn't is essential. SAM is distributed by Stanford University in an R-package. SAM identifies statistically significant genes by carrying out gene specific t-tests and computes a statistic ''dj'' for each gene ''j'', which measures the strength of the relationship between gene expression and a response variable.〔〔〔 This analysis uses non-parametric statistics, since the data may not follow a normal distribution. The response variable describes and groups the data based on experimental conditions. In this method, repeated permutations of the data are used to determine if the expression of any gene is significant related to the response. The use of permutation-based analysis accounts for correlations in genes and avoids parametric assumptions about the distribution of individual genes. This is an advantage over other techniques (e.g., ANOVA and Bonferroni), which assume equal variance and/or independence of genes.〔 ==Basic protocol== *Perform microarray experiments — DNA microarray with oligo and cDNA primers, SNP arrays, protein arrays, etc. *Input Expression Analysis in Microsoft Excel — see below *Run SAM as a Microsoft Excel Add-Ins *Adjust the Delta tuning parameter to get a significant # of genes along with an acceptable false discovery rate (FDR)) and Assess Sample Size by calculating the mean difference in expression in the SAM Plot Controller *List Differentially Expressed Genes (Positively and Negatively Expressed Genes) 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Significance analysis of microarrays」の詳細全文を読む スポンサード リンク
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