Significance Analysis of Microarrays

Significance Analysis Of Microarrays

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 d_j 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 (for example ANOVA and Bonferroni), which assume equal variance and/or independence of genes.