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    Insightful S+ArrayAnalyzer

    Category  Genomics>Gene Expression Analysis/Profiling/Tools

    Abstract   S+ArrayAnalyzer is specialized toolkit/module of S-PLUS
    that can be used for microarray analysis. S+ArrayAnalyzer (S+AA)
    provides comprehensive, rigorous statistical design and analysis of
    microarray experiments to help scientists identify differentially
    expressed genes, targets, and biomarkers. Because S+ArrayAnalyzer
    works within the S-PLUS environment, scientists can combine
    microarray analysis from S+ArrayAnalyzer with biostatistics models
    from S-PLUS to predict clinical outcomes and identify biomarkers.
    S+AA key features include:

    Integrated Data Access -

    S+AA includes flexible data access methods allowing you to load data
    via a graphical user interface or in batch mode using the 'Read Design'
    interface. The data import dialog allows you to specify different
    experimental designs and handles both Affymetrix and 2-color
    microarray data including: 1) Affymetrix GeneChip Operating Software
    (GCOS) summary data; 2) Affymetrix probe-level [cell intensity (CEL),
    Chip Definition File (CDF) and Probe] data; 3) Two-channel data
    including GenePix, Spot, ScanAlyze, and Agilent.

    S+AA includes the Affymetrix File and GCOS application programming
    interface (API), which allows you to rapidly read Affymetrix CEL, and
    summarized expression values (CHP) binary formats and to directly
    import from Affymetrix laboratory information management system
    (LIMS)/GCOS. S+AA can also be configured to read data directly from
    microarray databases such as, the Affymetrix Analysis Data Model
    (AADM) database, the Iobion Gene Traffic database and the Rosetta
    Resolver database. Imported Data is stored in the S-PLUS object
    database and managed visually through the S-PLUS object explorer.

    Quality Control Diagnostics and Filtering -

    S+AA provides an assortment of graphical tools for assessing the
    quality of your experimental data. The tools allow you to consider the
    quality of chips from several perspectives and to filter genes and chips
    based on these assessments. Diagnostic plots include: 1) Color
    image plot of the entire array; 2) M vs. A plot as either a scatter plot or a
    hexbin plot; 3) Genes Present plot; 4) Intensity boxplot; 5) RNA
    degradation plot; 6) Principal components plot.

    Advanced Normalization Methods -

    Normalization is the key to reducing variation in the measured gene
    expression levels. S+AA includes many advanced methods for
    normalization, including both within and between chip methods for two
    channel data and advanced methods for Affymetrix probe-level (CEL)
    and summary (CHP) data using non-linear methods such as quantiles.

    Precise and Powerful Statistical Tests -

    A key goal of microarray experiments is to identify genes that are
    differentially expressed. S+AA includes the leading statistical methods
    for identifying differentially expressed genes, as well as many methods
    for class discovery and prediction. Methods for differential expression
    include: 1) Two (2) sample and paired t-tests; 2) Wilcoxon test; 3)
    Distribution and Permutation based tests; 4) One-way and two-way
    analysis of variance (ANOVA) [fast, scaleable linear model methods];
    5) Local pooled error testing (LPE).

    Leading Clustering Methods -

    S+AA includes a vast set of partitioning and hierarchical cluster
    analysis methods. Hierarchical methods allow complete, average and
    single linkage, and a variety of distance metrics e.g. Euclidean,
    manhattan, maximum and binary. Partitioning methods include k-
    means, and a robust partitioning around medoids method. Model
    based clustering, whereby a set of multivariate Gaussian mixtures are
    fit in a Bayesian context, is also available. A number of other
    unsupervised learning methods are available in S-PLUS including self-
    organizing maps, fuzzy clustering and additional agglomerative
    methods (agnes) and divisive methods (diana and mona).

    Control of Family Wise Error Rate and False Discovery Rate -

    S+AA includes many methods for controlling the family wise error rate
    (FWER) and the false discovery rate (FDR). The FWER is controlled by
    using adjusted p-values for each gene so the overall Type I error rate
    is maintained at a desired level.

    Methods available for controlling FWER include: 1) Bonferroni; 2)
    Hochberg (1988); 3) Holm (1979); 4) Westfall & Young (1993).

    Methods available for controlling FDR include: 1) Benjamini and
    Hochberg (1995); 2) Benjamini and Yekutieli (2001).

    Annotation and Gene List Management -

    The gene list represents the transition from the statistical analysis to
    the biological interpretation. There is a great deal of available
    annotation metadata available to help with the inferential and
    interpretive process.

    S+AA uses annotation metadata in four (4) main ways:

    1) Annotate graphical and tabular reports from statistical analyses
    using gene lookup metadata sites, such as LocusLink and Entrez.

    2) Annotate gene lists derived from the statistical analyses via
    metadata repositories such as LocusLink, Entrez, Pubmed, AmiGO
    and Source.

    3) Connect to gene list analysis sites such as Onto-Express and
    DAVID/EASE, and initiate gene list analyses [e.g., gene function
    enrichment and identification of gene ontology (GO) categories that are
    overrepresented in gene lists derived from statistical analyses].

    4) Subset microarray datasets according to GO categories prior to
    (differential expression) analysis.

    S+AA also includes flexible methods for gene list management
    including tools for combining and comparing gene lists. Standard
    Venn diagrams provide a helpful visual in this process but represent
    only the tip of the underlying functionality available.

    Graphical and Tabular Reports -

    S+AA includes a rich palette of interactive and publication quality
    graphical and tabular reports. Graphics include volcano plots, parallel
    coordinate plots, whole genome plots, heat maps, silhouette plots,
    principal component biplots and Venn diagrams. Interactive reports
    are hyperlinked to gene annotation metadata and summary
    information e.g. LocusLink, Entrez, Pubmed, AmiGO and Source.

    Open and Extensive Development Environment -

    S+AA leverages the S-PLUS language, which is a full featured object-
    oriented language for the analysis of data. Every feature available via
    the graphical user interface has an accessible programmatic
    command (function). You can use these functions to build scripts for
    automated analysis, batch analysis, or prototyping/implementing new
    methods. In addition to the S-PLUS language S+ AA also offers a Java
    and C++ application programming interface (API). These API's allow
    you to further extend S+AA by creating custom interfaces, connections
    to other software, or integrating within your customized workflow.

    System Requirements  S+ArrayAnalyzer is supported on the following
    platforms:

    • Windows NT 4.0 Service Pack 6 or later
    • Windows 2000
    • Windows XP Professional
    • Windows ME
    • Windows 98

    The minimum recommended system configuration is a Pentium II/
    300 processor, at least 512MB of RAM, and an SVGA or better
    graphics card and monitor. You must have at least 225MB of free disk
    space for the typical installation (and, even if not installing on drive
    C:\, an additional 2MB of free disk space on drive C:\ to unpack the
    distribution).

    Manufacturer   Home office; see web site for international locations.

    Insightful Corporation
    1700 Westlake Avenue N, Suite 500
    Seattle, WA 98109-3044
    USATel: 206.283.8802, 800.569.0123 ext. 479
    Fax: 206.283.8691
    info@insightful.com
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