SAM 4.01 – Significance Analysis of Microarrays

SAM 4.01


SAM (Significance Analysis of Microarrays) is a statistical technique for finding significant genes in a set of microarray experiments, a supervised learning software for genomic expression data mining.

The input to SAM is gene expression measurements from a set of microarray experiments, as well as a response variable from each experiment. The response variable may be a grouping like untreated, treated [either unpaired or paired], a multiclass grouping (like breast cancer, lymphoma, colon cancer, . . . ), a quantitative variable (like blood pressure) or a possibly censored survival time.


Stanford University Statistics and Biochemistry Labs







Jun Li and Robert Tibshirani (2011)
Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data.
Stat Methods Med Res. 2011 Nov 28.

Leave a Reply

Your email address will not be published. Required fields are marked *


This site uses Akismet to reduce spam. Learn how your comment data is processed.