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    SAM (Significance Analysis of Microarrays)

    Category  Genomics>Gene Expression Analysis/Profiling/Tools

    Abstract  SAM (Significance Analysis of Microarrays) is supervised
    learning software product that can be used for genomic expression
    data mining/analysis. SAM is a statistical technique for finding
    significant genes in a set of microarray experiments. It was proposed
    by Tusher, Tibshirani and Chu. The software was written by
    Balasubramanian Narasimhan and Robert Tibshirani. 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. SAM computes a
    statistic di for each gene i, measuring the strength of the relationship
    between gene expression and the response variable. It uses repeated
    permutations of the data to determine if the expression of any genes is
    significantly related to the response. The cutoff for significance is
    determined by a tuning parameter 'delta', chosen by the user based on
    the false positive rate. One can also choose a 'fold change' parameter,
    to ensure that called genes change at least a pre-specified amount.

    Product features/capabilities include:

    1) SAM is a convenient Excel Add-in.

    2) SAM can be applied to data from Oligo or cDNA arrays, SNP arrays,
    protein arrays, etc.

    3) SAM correlates expression data to clinical parameters including
    treatment, diagnosis categories, survival time, paired (before and
    after), quantitative (e.g. tumor volume) and one-class. Both parametric
    and non-parametric tests are offered.

    4) SAM correlates expression data with time, to study time trends. The
    experimental units can fall into one or two classes, or be paired.

    5) SAM provides automatic imputation of missing data via nearest
    neighbor algorithm (Upgraded in SAM version 2.0).

    6) Its adjustable threshold determines the number of genes called
    significant.

    7) SAM uses data permutations to provide an estimate of the False
    Discovery Rate (FDR) for multiple testing.

    8) SAM's version 2.0 reports local false discovery rates and missing
    rates (see Note 1).

    9) SAM can deal with 'blocked designs', for example, when treatments
    are applied within different batches of arrays.

    10) SAM provides 'pattern discovery' via Eigen genes (principal
    component analysis).

    11) SAM provides 'gene lists' in Excel workbook form, exportable into
    TreeView, Cluster or other software systems (see Note 2).

    12) SAM listed genes are web-linked to Stanford SOURCE database.

    13) SAM was developed at Stanford University Statistics and
    Biochemistry Labs.

    14) SAM provides a 41 page users guide and technical document in
    electronic form Portable Document Format (PDF).

    Note 1: Major New release 3.0, Jan 23, 2007. SAM now offers gene set
    analysis.

    Note 2: Charlie Kim of Stanford's Falkow lab has written a neat tool
    called "samster''. It takes the significant gene list from SAM and exports
    it into a file readable by either TreeView or Cluster (Mike Eisen's
    programs).  Samster is available at Falkow lab software  at:  http:
    //falkow.stanford.edu/whatwedo/software/software.html

    System Requirements  

    SAM 2.0 requires
    • R version 2.0 or higher from CRAN
    • Excel 2000 or higher.
    • Windows 2000 or XP.
    SAM version 1.21 and earlier required the Microsoft Java VM but current
    versions have eschewed Microsoft Java.

    Manufacturer   

    Stanford University Statistics and Biochemistry Labs
    Department of Statistics -- Sequoia Hall
    390 Serra Mall
    Stanford University
    Stanford, CA 94305-4065
    Phone: (650) 723-2620
    Fax: (650) 725-8977
    sam-bug@stat.stanford.edu
    http://groups.yahoo.com/group/sam-software
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