Significance Analysis of Microarrays

SAM: Significance Analysis of Microarrays

Supervised learning software
for genomic expression data mining

News

New release, December 19, 2014. SAM is now cross-platform and therefore works on Windows and Macs the same way. There is no need to download it. Please visit the SAM Project Github Site for instructions.

New release 4.01, Dec 27, 2013. SAM now works with 64-Bit Windows 7

Major new release 4.0, July 1, 2011. SAM now handles RNAseq data, using the ``SAMSeq'' method described in Jun Li and Robert Tibshirani. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. To appear, Statistical Methods in Medical research.

SAM works on MACs. See MAC instructions

Major New release 3.0, Jan 23, 2007. SAM now offers gene set analysis, as described in
On testing for the significance of sets of genes (Efron and Tibshirani, 2007, to Appear, Annals of Applied Statistics vol 1.) .
This is a variation of Gene Set Enrichment Analysis .

How does Gene set analysis differ from Gene set enrichment analysis?
See also the gene set collections at GSA homepage

Major New Release: Version 2.0. June 6, 2005. Now version 2.11---- Aug 24, 2005. All users should upgrade to this version. SAM now handles time course data, does non-parametric tests and pattern discovery, It also reports local false discovery rates and miss rates.

New release 2.20, Oct 4, 2005. SAM now provides sample size assessment- estimates of FDR, FNR, type I error and power for different sample sizes.
"A simple method for assessing sample sizes in microarray experiments" (pdf) .

A discussion and annoucement group for all SAM-related discussions and announcements has been created. See http://groups.yahoo.com/group/sam-software.

Features

SAM manual
Developed at Stanford University Labs: based on recent paper of Tusher, Tibshirani and Chu (2001):
"Significance analysis of microarrays applied to the ionizing radiation response" (ps file). (pdf version). PNAS 2001 98: 5116-5121, (Apr 24). "Raw data"
Correlates gene expression data to a wide variety of clinical parameters including
treatment, diagnosis categories, survival time and time trends
Provides estimate of False Discovery Rate for multiple testing
Convenient Excel Add-in
Works with data from both cDNA and oligo microarrays. Can also be applied to protein expression data and SNP chip data.
Patent Pending for SAM technology
SAM uses the FDR and q-value method presented in Storey (2002) A direct approach to false discovery rates. J. Roy. Stat. Soc. Ser. B, 64:479-498;

Local false discovery rates proposed in Efron, B., Tibshirani, R., Storey, JD, and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment, JASA, 96, 1151-1160 and Efron and Tibshirani, Microarrays, Empirical Bayes Methods, and False Discovery Rates" Genet. Epidemiol. 2002 Jun;23(1):70-86;

and Miss rates--- Jon Taylor, Rob Tibshirani and Brad Efron. The ``Miss rate'' for the analysis of gene expression data; Biostatistics 2005 6(1):111-117.