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==Significance analysis of microarrays (SAM)==
[[Image:SAM.png|thumb|right]]
'''Significance analysis of microarrays (SAM)''' is a [[statistics|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 microarray]]s, 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 (programming language)|R-package]].{{cn}}
SAM identifies statistically significant genes by carrying out gene specific [[Student's t-test|t-tests]] and computes a statistic ''d<sub>j</sub>'' for each gene ''j'', which measures the strength of the relationship between gene expression and a response variable.<ref name="R1">Chu, G., Narasimhan, B, Tibshirani, R, Tusher, V. "SAM "Significance Analysis of Microarrays" Users Guide and technical document." [http://www-stat.stanford.edu/~tibs/SAM/sam.pdf]</ref><ref name="R7"/><ref name="R8"><Zhang, S. (2007). "A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance." BMC Bioinformatics 8: 230.</ref> 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 [[wikt:Special:Search/parametric|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.<ref name="R6"/>
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