Significance Analysis of Microarrays - Algorithm

Algorithm

SAM calculates a test statistic for relative difference in gene expression based on permutation analysis of expression data and calculates a false discovery rate. The principal calculations of the program are illustrated below.

The s_o constant is chosen to minimize the coefficient of variation of d_i. r_i is equal to the expression levels (x) for gene i under y experimental conditions.

Fold changes (t) are specified to guarantee genes called significant change at least a pre-specified amount. This means that the absolute value of the average expression levels of a gene under each of two conditions must be greater than the fold change (t) to be called positive and less than the inverse of the fold change (t) to be called negative.

The SAM algorithm can be stated as:

1. Order test statistics according to magnitude
2. For each permutation compute the ordered null (unaffected) scores
3. Plot the ordered test statistic against the expected null scores
4. Call each gene significant if the absolute value of the test statistic for that gene minus the mean test statistic for that gene is greater than a stated threshold
5. Estimate the false discovery rate based on expected versus observed values