SAM: Significance Analysis of Microarrays

      Supervised learning software
         for genomic expression data mining


News

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) .

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.

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.

Obtaining SAM

Note added October 2013. Due to the RDCOM server not being available for R anymore, we have had to rewrite SAM significantly. This is almost done and we expect to release a new version by mid November 2013. Our sincere apologies for any inconvenience caused. In an emergency, the R package samr can be used, although it is command-line driven.
  • Academic users can download SAM by going directly to the registration page. Please note that this is the full version!
  • Non academic users should first register via the registration page. An evaluation version (limited to 500 genes) can be downloaded directly from that page.

    If you are a commercial user and wish to obtain a complete version of SAM, proceed to the SAM resource at the Office of Technology and Licensing. The SAM contact is Kirsten Leute (Kirsten.Leute@stanford.edu) at the Office of Technology and Licensing, Phone: (650) 723-4374.

    Please do not contact Kirsten Leute about downloading, technical questions etc. All she handles is commercial licensing!

  • Returning users (those who have already registered) who want to download the software again can proceed directly to the Academic Download Page or the Non-Academic Download Page. You will need the registration information that you received via email.