Features
S Programming Language
The award-winning S programming language is at the core of S-PLUS. The only language created specifically for exploratory data analysis and statistical modeling, the S programming language allows you to create statistical applications up to five times faster than with other languages.
- Object-oriented, interpreted 4GL language
- Interactive exploration and fast prototyping
- Rich data structures: vector, matrix, array, data frame, list and many more
- User-defined functions, objects, classes, methods and libraries
- Library of over 4000 functions for data manipulation, graphics, statistical modeling, and integration
- CSAN library of available packages
S-Plus Workbench Development Environment
Rapidly create reliable statistical applications with this integrated development environment for S programmers.
- Based on industry-standard Eclipse framework
- Check-in and check-out files with source code control system integration
- Intelligent editor for S programs with line numbering, automatic indentation, and syntax highlighting
- Project, file and task management
- Automatic syntax error detection
- Code outline browser
- Command-line console with history recall
- Object and search path views
- Analytic step-by-step debugger
- Analytic profiling
- Package system for improved porting and deployment
Graphical User Interface
A convenient window-based GUI puts common tasks at your fingertips with easy-to-use menus and dialogs
- File import and export dialogs
- Database import and export dialogs¹
- Dialogs for data preparation, charting and statistical modeling
- Interactive command-line with history recall
- Manage objects with Object Explorer¹
- Script file editor¹
- Multiple data and graphics windows
- Cut-and-paste to Word, PowerPoint and Excel¹
- Integrated Excel spreadsheets¹
- PowerPoint Wizard: quickly create slides from charts¹
- Create custom toolbars, menus and dialogs¹
- On-line help and manuals
- Eclipse based development environment
Scalable Pipeline Architecture
Scale statistical applications to gigabytes of data without the need for additional RAM or 64-bit architectures with this library of data types and functions for programming with large data sets.
- Data types for out-of-memory vectors, data frames, and time series
- Use familiar S functions, operators and programming style
- Scalable algorithms for data manipulation, charting and modeling
- High-performance data preparation tools: aggregate, merge, sort, partition, filter and more
- Data manipulation using built-in SQL processor
- Hexagonal binning plots to explore structure of large data sets
- Scalable model estimation: univariate statistics, linear regression, analysis of variance, logistic regression, poisson regression, quasi-likelihood, K-means clustering, principal components
- Scalable model scoring for more than 20 model types
Graphical Functions
Explore data and create custom charts with this library of graphical functions in the S language
- Scatterplots, histograms, pie charts, box plots, bar charts, dot charts, time series charts, 3-D wireframe charts, image plots and many more.
- Brush and spin dynamic visualization
- Programmatic control over colors, lines, axes, annotations and layout
- Unique Trellis™ graphics – create multiple charts conditioned by levels of one or more variables
- Create interactive, embedded web-based charts with S-PLUSGraphlets™
- Element-Specific Graph arguments for plots and command-line graphics
Integration
S-PLUSis an open system, designed to integrate with the systems you already have.
Data and graphics formats
- ASCII: fixed format, comma-separated, and tab-delimited
- Spreadsheets: Excel, Lotus 1-2-3, Quattro Pro
- Application data: SAS 7/8/9, SPSS, Matlab, Minitab, Sigma Plot, Systat, STATA, Gauss, Epi Info and more
- Database files: Paradox, dBase, Access, FoxPro
- Financial data sources: LIM, Bloomberg, FAME
- Native database clients: SQL Server¹, Oracle, Sybase, IBM DB2
- ODBC interface to compliant databases
- Export graphics as PDF, PostScript, GIF, PNG, JPG, WMF, bitmap, TIFF and more
APIs and system interfaces
- APIs for C, C++, Java and Fortran
- Language support for pipes, sockets, and files
- DDE, COM and OLE interfaces¹
- XML import and export
- Reporting in XML, PDF, HTML and RTF
Statistical & Numerical Techniques
S-PLUSis the most comprehensive statistical analysis package available, and includes all of the following capabilities:
Basic Statistics
- Summary statistics
- Crosstabulations
- Correlation and covariance
- Probabilities, quantiles, densities and random number generation from many distributions
- Durbin-Watson statistic
Hypothesis Tests and Confidence Intervals
- One-sample and two-sample t-test and Wilcoxon
- Paired t-test
- Correlation: Pearson, Kendall's tau, Spearman's rho
- Goodness-of-Fit: Chi-square, Kolmogorov-Smirnov, Shapiro-Wilk
- Rank tests: Kruskal-Wallis, Friedman
- Proportions: exact Binomial test, Normal approximation
- Contingency tables and tests for independence: Chi-square, Fisher, Mantel-Haenszel, McNemar
Regression
- Basic linear regression
- Polynomial regression
- Model diagnostics
- Prediction and confidence intervals
- Stepwise selection of models
- Parametric spline models
- Constrained regression
- Logistic regression
- Generalized linear models
Analysis of Variance
- Univariate and multivariate ANOVA
- Flexible specification of variables, covariables, interactions, nesting, transformations
- Automatic generation of dummy variables
- Choice of contrasts
- Type III sums of squares
- Designed experiments: one-way, two-way, factorial, split-plot, unbalanced, fractional factorial designs, response surface methods, robust designs, taguchi methods and more
- Variance component estimation
- Multiple comparisons: Fisher, Tukey, Dunnett, Sidak, Bonferroni, Scheffé, simulation-based
Nonlinear Regression and Maximum Likelihood
- Nonlinear regression
- Nonlinear maximum likelihood
- Quasi-likelihood
- Constrained nonlinear regression
Nonparametric Regression
- Generalized additive models (GAMs)
- Smoothers: loess, super, kernel, spline
- Projection Pursuit, ACE, and AVAS
Tree Models
- Classification trees
- Regression trees
- Pruning, shrinking, and splitting
- Scoring
Correlated Data Analysis
- Longitudinal data and repeated measures analysis
- Linear (LME), nonlinear (NLME), and generalized mixed effects (GLMM) models
- Generalized Estimating Equations (GEE)
- Biexponential, first-order compartment, four-parameter logistic models
- User-defined correlation structures
Resampling
- Bootstrap
- Jackknife
Multivariate Analysis
- Canonical correlation
- Discriminant analysis
- Factor analysis
- Multidimensional scaling
- Principal components
- Biplots
Cluster Analysis
- K-means
- Hierarchical clustering
- Monothetic clustering
- Model-based clustering
- Crisp and fuzzy clustering
- Divisive and agglomerative methods
Quality Control
- Shewhart chart
- Cusum chart
- Charts based on xbar, s, np, p, c, u
Power and Sample Size
- Normal mean
- Binomial proportion
Survival Analysis
- Kaplan-Meier curves
- Cox proportional hazards models with mixed effects
- Left, right, and interval censoring
- Time-dependent covariates and strata
- Multiple event models
- Competing risk models
- Frailty models
- Parametric survival
- Expected survival
- Person years analysis
- Aalen's Additive Regression Model
Time Series Analysis
- Autocovariance, autocorrelation and partial autocorrelation
- Smoothed periodograms
- Box-Jenkins ARIMA models
- Classical and robust AR
- Long-memory models
- Seasonal decompositions
- Fourier transformations
- Classical and robust smoothers and filters
Robust Statistics
- Robust estimation and inferences
- Robust MM regression
- Robust GLM, ANOVA, covariance, principal components, and discriminant analysis
- Least trimmed squares regression
- Minimum absolute residual regression
- Visually compare robust and traditional methods
Missing Data
- Multiple imputation
- Gaussian, logistic, and conditional Gaussian models
Date, Time, and Calendar Data
- Univariate and multivariate time series
- Aggregation, alignment, merging, and interpolation
- Times and dates from milliseconds to millennia
- Time zones with international daylight savings rules
- Holidays and financial market closures
- Custom time and date formats
- Relative time, time sequence, and event objects
- Powerful time-series charting
Mathematical Computations
- Vector and matrix algebra
- Matrix decompositions
- Systems of linear equations
- Locate roots
- Nonlinear optimization
- Constrained optimization
- Ordinary differential equations
- Numerical integration
Additional Libraries
Libraries from Insightful Research and the S-PLUSuser community offer additional capabilities
- MASS: Modern and Applied Statistics libraries (Venables, Ripley) included
- Hmisc and Design libraries for biostatistical and epidemiologic modeling (Harrell) included
- Insightful Research libraries available for download
Optional modules add additional capabilities to S-PLUS:
- S+ArrayAnalyzer: microarray analysis¹
- S+EnvironmentalStats: environmental statistics¹
- S+FinMetrics: financial econometrics
- S+NuOPT: large-scale constrained optimization
- S+SeqTrial: Clinical trial design and analysis¹
- S+SpatialStats: analysis of spatial data
- S+Wavelets: wavelet and signal series analysis
- Fame S+ Connector: accelerate the process of bringing accurate data from FAME into the powerful S-PLUSenvironment for advanced quantitative analysis.
Supported Platforms
- Windows 2000,Windows XP, Windows Vista
- Sun Solaris (SPARC)
- Red Hat and SUSE Linux (Intel)
¹ Windows Only
Feature List
Import Data Easily
S- PLUS for ArcView GIS integrates the powerful statistics and publication-quality graphics of S- PLUS into ArcView GIS . Easily transfer data and results S- PLUS for ArcView GIS makes it easy to transport data and results between the two products. Seamlessly move ArcView GIS tabular data to S- PLUS data frames and then return your graphics and analytical results to the ArcView GIS environment. S- PLUS objects are directly accessible from ArcView GIS. For simple or complex projects, for novice or advanced analysts, S- PLUS for ArcView GIS offers the ability to quickly and easily extract valuable meaning from your GIS data.
Unparalleled Graphics
Stunning, Publication-Quality Graphs With S- PLUS for ArcView GIS , you benefit from over eighty 2D and 3D publication-quality graph types available in S- PLUS . With so many options, you can choose the graph type which best represents your data. S- PLUS graphics are easily edited so you have complete control over every detail- just point-and-click to modify any feature of your graph. Unique and Powerful TrellisT graphics The unique visualization techniques in S- PLUS , such as Trellis graphics, offer you a powerful new way to explore your GIS data.
With Trellis graphics, you can immediately discover important relationships between two or more variables by segmenting the relationship based on a conditioning variable. By comparing the graphical representation for subsets of your conditioning variable, you gain new insight into your data. Classical and modern statistical methods You get more out of your data with the powerful S- PLUS statistical functionality.
Superior Analytical Power
With over 2000 functions, S- PLUS has a complete range of classical and modern statistical functions. Use S- PLUS functions to explore your data, find hidden outliers or exceptional values, or spot trends. You can learn more, get better results, and make better decisions. Cutting-edge spatial statistics Additionally, S- PLUS for ArcView GIS provides access to spatial statistics in S+SpatialStatsT, an add-on module to S- PLUS . With S+SpatialStats , users can easily access comprehensive spatial data analysis and spatial statistical modeling tools for geostatistical data, lattice data and spatial point patterns.
Analyze and Visualize Spatial Data
In addition to standard S-PLUSstatistics, S-PLUSfor ArcView GIS allows you to access spatial statistics available in S+SpatialStats . Features Fit linear regression models in ArcView GIS using data from ArcView themes or S-PLUSdata sets Plot bar and pie charts in ArcView GIS based on S-PLUSdata Export attribute data into S-PLUS(optionally calculate polygon centroid coordinates) Import S-PLUSdata into ArcView GIS tables or point themes Import S-PLUSgraphs onto a layout in ArcView Basic S-PLUScommand line interface from within ArcView GIS Preserve groupings and selections made in ArcView GIS for in-depth analysis in S-PLUS.
Feature List
Geostatistical Data:
- Contour plots
- 3D point clouds
- Variogram plots and box plots
- Directional variograms and correlograms for exploring anisotropy
- Empirical variogram estimation including robust methods
- Variogram models including spherical and exponential
- Ordinary and universal kriging
- Block and Point Kriging prediction at arbitrary locations with standard errors
- Parametric and nonparametric trend surfaces
Point Patterns:
- Point maps that include region boundaries
- Spatial randomness tests
- Ripley's K-functions
- Simulation of spatial random processes
- Local intensity estimation
Lattice Data:
- "Binning" of high density data into a regular lattice of counts
- Geary and Moran spatial autocorrelation coefficients
- Spatial regression models including conditional and simultaneous autoregressive models
- Nearest neighbor search
- Visualization of neighbor structures
Feature List :
- Discrete wavelets transform
- Wide choice of wavelets basis functions
- Multi-resolution decomposition and analysis
- Non-decimated wavelet transforms
- Time-frequency graphical displays
- Optimum nonlinear extraction of non-smooth signals from noise
- Robust wavelets analysis
- Wavelet packet analysis
- Best-basis adaptive choice of transform
- Matching pursuit decompositions
- 1D and 2D data support with arbitrary sample sizes
- Full range of boundary correction methods appropriate for your data
Feature List
Pull-Down Menus and Dialogs: Perform Your Analyses via the New Pull-Down Menus
Probability Distributions: Compute Densities, Probabilities, Quantiles, and Random Numbers for the Following Distributions
- Continuous Distributions: Beta, Cauchy, Chi (square root of a chisquare), Chisquare, Empirical, Exponential, Extreme Value, Generalized Extreme Value, F (central and non-central), Gamma, Logistic, Lognormal, 3-Parameter Lognormal, Mixture of Two Lognormals, Truncated Lognormal, Normal, Mixture of Two Normals, Truncated Normal, Pareto, Stable, Student's t (central and non-central), Triangular, Uniform, Weibull
- Discrete Distributions: Binomial, Empirical, Geometric, Hypergeometric, Negative Binomial, Poisson, Wilcoxon
- Mixtures of Continuous and Distrete Distributions: Zero-Modified Lognormal (Also Called the Delta Distribution; Lognormal with positive mass at 0), Zero-Modified Normal (Normal with positive mass at 0)
Probability Density and Cumulative Distribution Plots
- Plot PDFs and CDFs so you can see how they change with the value of the distribution parameter(s)
Summary Statistics
- Several additional summary statistics have been added to the ones already available in S-PLUS
Q-Q Plots for All Probability Distributions
- Includes Standard Q-Q Plots and Tukey Mean-Difference Plots
Q-Q Plot Gestalt Function That Produces Numerous "Typical" Q-Q Plots for a Specified Distribution
- Allows You to Build Up a Visual Memory of "Typical" Q-Q Plots
Estimation of Distribution Parameters and Quantiles
- Several Estimation Methods Available: Maximum Likelihood, Minimum Variance Unbiased, Method of Moments, Method of L-Moments, etc.
- Results Printed in "Nice" Format: Data Set Name, Sample Size, Method of Estimation, Optional Confidence Interval
Confidence Intervals for Distribution Parameters
- Binomial: Exact, Normal Approximation
- Exponential: Exact
- Extreme Value: Normal Approximation
- Lognormal: Exact (Land, 1971), Parkin et al.'s (1990) Approximation, Cox's
- Approximation (Land, 1972), Normal Approximation
- Three-Parameter Lognormal: Normal Approximation, Likelihood Profile, Zero
- Skewness (Royston, 1992b)
- Normal: Exact
- Poisson: Exact, Pearson-Hartley Approximation, Normal Approximation
- Zero-Modified Lognormal (Delta): Normal Approximation
- Zero-Modified Normal: Normal Approximation
Confidence Intervals for Distribution Quantiles
- Lognormal
- Normal
- Poisson
- Nonparametric
Goodness-of-Fit Tests (New and Updated)
- Chi-Square, Kolmogorov-Smirnov, Probability Plot Correlation Coefficient, Shapiro-Francia, Shapiro-Wilk
- Allow User to Estimate the Distribution Parameters
- Results Printed in "Nice" Format: Data Set Name, Hypothesized Distribution, Estimated Parameters, Test Method
- Results Can Be Plotted. Optional Plots Include: Histogram with Overlaid Fitted Distribution, Q-Q Plot, CDF Plots of Observed and Fitted Distribution, Test Results
Optimal Box-Cox Transformations: Determine Optimal Power Transformation Based on Probability Plot Correlation Coefficient or Other Criteria
Prediction and Tolerance Intervals
- Lognormal
- Normal
- Poisson
- Nonparametric
Special Hypothesis Tests
- Chen's Modified One-Sided t-Test for Skewed Distributions
- Fisher's One-Sample Randomization (Permutation) Test for Location
- Quantile Test (Detects Shifts in Tail of Distribution)
- Two-Sample Linear Rank Tests
- Test for Serial Correlation Based on von Neumann Rank Test
- Seasonal Kendall Test for Trend
Power and Sample Size Calculations for Standard Hypothesis Tests
- Includes Sample Size, Power, Minimal Detectable Difference, and Significance Level
- Functions to Easily Plot These Quantities
Calibration
- Fit a Calibration Line or Curve
- Predict Concentrations Based on Fitted Calibration Curve and Compute Associated Confidence Intervals
- Determine Decision and Detection Limits
Methods for Type I Censored Data
- Empirical Cumulative Distribution Plots
- Quantile-Quantile (Probability) Plots
- Goodness-of-Fit Tests
- Parameter/Quantile Estimation and Confidence Intervals
- Prediction and Tolerance Intervals
- Hypothesis Testing
Tools for Probabilistic Risk Assessment
- Simple Random Sampling and Latin Hypercube Sampling
- Generate Random Numbers from a Multivariate Normal Distribution
- Generate a Multivariate Matrix from One or More Specified Distributions with a
- Specified Rank Correlation
- Create an Output Distribution of Exposure or Risk
Built-In Data Sets
- Data Sets Appearing in Selected EPA Guidance Documents
- Selected Data Sets from the Environmental Statistics Literature
Extensive Hypertext Help System
- Cross-Referenced Help Files that Clearly Explain Each Procedure and Provide Specific, Detailed Examples
- Detailed Abstracts of Selected Literature in Environmental Statistics
- A Fully Cross-Referenced, Hypertext Glossary of Statistical and Environmental Terms
Feature List
Statistics
- Statistical Summaries and Tests
- Extreme Value Theory
- Copula Modeling and Estimation
Time Series Tools
- Complete suite of Date and Calendar Time Series Objects
- Aggregation and Disaggregation
- Missing Value Interpolation
- Technical Indicators
- Intra-day Moving Average
- Factor ARCH models, Engle et al
- Matrix/matrix models
Econometric Estimation
- Generalized Method of Moments
- Efficient Method of Moments
- Linear and nonlinear SUR
- Vector Autoregressive Models (VARs)
- Bayesian VARs
- Vector Error Correction Models
Complex Dynamic Models
- Long memory models
- State space models
- Nonlinear regime switching models
Strategies
- Rolling Estimation and Backtesting
- Multifactor Models
- Fixed Income Analysis
Feature List
- Complete set of powerful and efficient solvers included
- Very fast and robust optimization
- Optimize problems with thousands of variables and constraints
- Complete flexibility in specifying objective function and constraints
- Specify problems in a natural way using SIMPLE, a class of objects in the S language
- Automated symbolic differentiation of objective function
- Complete control over solver tolerance and options
Optimization Problems Solved
- Linear Programming
- Mixed-Integer Linear Programming
- General Convex Programming
- Convex Quadratic Programming
- General Non-Linear Programming
- Mixed Integer Quadratic Programming
Optimization Methods
- Primal-dual interior point method with higher-order correction for linear programming
- Simplex method for linear programming
- Primal-dual interior point method based on line search for general convex Programming models including convex quadratic programming models
- Primal-dual interior point method based on trust region for general non-linear programming models
- Primal-dual interior point method based on quasi-Newton method for general non-linear programming models
- Active set method for convex quadratic programming models and mixed integer quadratic programming models
SIMPLE: A set of classes for defining optimization problems
- "Set" class: element, union, intersection, difference, direct product
- Defining objective functions: expressions, variables, integer variables, parameters.
- Defining contraints: relational, equality, inequality, inclusion in set, conditional expressions.
Feature List
Integrated Data Access
S+ArrayAnalyzer 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:
- Affymetrix GeneChip® MAS 4/5 summary data
- Affymetrix probe-level (CEL, CDF and Probe) data
- Two-channel data including GenePix, Spot, ScanAlyze, and Agilent
S+ArrayAnalyzer includes the Affymetrix File and GCOS application programming interface (API), which allows you to rapidly read Affymetrix CEL, and CHP binary formats and to directly import from Affymetrix LIMS/GCOS. The Affymetrix File and GCOS API provides an intermediate layer so that whenever Affymetrix updates its data formats, S+ArrayAnalyzer immediately adapts to this, resulting in no downtime for any users of the S+ArrayAnalyzer system.
S+ArrayAnalyzer can also be simply configured to read data directly from microarray databases such as the Affymetrix AADM database, the Iobion Gene Traffic database and the Rosetta Resolver database.
Imported Data is stored in the S-PLUSobject database and managed visually through the S-PLUSobject explorer.
Quality Control Diagnostics and Filtering
S+ArrayAnalyzer provides an assortment of graphical tools for assessing the quality of your experimental data. The tools allow you to consider quality of chips from several perspectives and to filter genes and chips based on these assessments. Diagnostic plots include:
- Color image plot of the entire array
- M vs. A plot as either a scatter plot or a hexbin plot
- Genes Present plot
- Intensity boxplot
- RNA degradation plot
- Principal components plot
Advanced Normalization Methods
Normalization is the key to reducing variation in the measured gene expression levels. S+ArrayAnalyzer 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.
In two-channel arrays, the main pre-processing required is normalization within slides for balancing intensities between channels/dyes. The standard method is to normalize using a smooth function of intensity e.g. the loess() function in S-PLUS. This approach may also be used to remove spatial effects of print-tips by fitting a separate loess() function for each print-tip. Two-channel, within-chip normalization methods comprise: median, loess, 2-D loess, print-tip loess, MAD, global MAD, print-tip, MAD 2-channel. Between-chip methods comprise: vsn, quantiles on R/G, quantiles on A.
In the Affymetrix system the goal is to line up the distribution of values from individual chips. Methods for CEL data comprise: quantiles, quantiles with robust option, invariant set, constant, contrasts, loess and vsn. Methods for MAS summary data comprise: median, inter-quartile range, vsn, quantiles and scale.
Precise and Powerful Statistical Tests
A key goal of microarray experiements is to identify genes that are differentially expressed. S+ArrayAnalyzer 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:
- Two sample and paired t-tests
- Wilcoxon test
- Distribution and Permutation based tests
- One-way and two-way anova (fast, scaleable linear model methods)
- Local pooled error testing (LPE)
The local pooled error test (LPE) is designed specifically for low-replicate microarray experiments. The LPE test statistic for each gene is formed by pooling variance estimates locally (i.e. just for genes with very similar expression intensities) from replicated arrays within experimental conditions. The LPE approach handles the situation where a gene with low expression may have very low variance by chance and the resulting signal-to-noise ratio is unrealistically large. The LPE method works very well in cases where RNA is limited or the budget doesn't allow many replicate chips to be run. In combination with a resampling FDR correction, the LPE method has been shown to outperform other 2-sample comparison methods.
The linear models methods in S+ArrayAnalyzer e.g. ANOVA and nested models use fast, scaleable algorithms, optimized to the high-throughput data array format of microarray technology.
Leading Clustering Methods
S+ArrayAnalyzer 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 kmeans, 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-PLUSincluding 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+ArrayAnalyzer 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 family wise error rate include:
- Bonferroni
- Hochberg (1988)
- Holm (1979)
- Westfall & Young (1993)
Methods available for controlling FDR include:
- Benjamini and Hochberg (1995)
- 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+ArrayAnalyzer uses annotation metadata in four main ways:
- Annotate graphical and tabular reports from statistical analyses using gene lookup metadata sites, such as LocusLink and Entrez.
- Annotate gene lists derived from the statistical analyses via metadata repositories such as LocusLink, Entrez, Pubmed, AmiGO and Source.
- 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 GO categories that are overrepresented in gene lists derived from statistical analyses).
- Subset microarray datasets according to GO categories prior to (differential expression) analysis.
S+ArrayAnalyzer 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+ArrayAnalyzer leverages the S-PLUSlanguage, 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. This gives you full control over the analysis unlike many black box applications. In addition to the S-PLUSlanguage S+ ArrayAnalyzer also exposes a Java and C++ application programming interface (API). These API's allow you to further extend S+ArrayAnalyzer by creating custom interfaces, connections to other software, or integrating within your customized workflow.
Flexible Deployment
S+ ArrayAnalyzer is capable of adapting to your needs and can be deployed in a variety ways. Typically these decisions are by taking into account various factors such as number of users, size of data, analysis workflow, reporting requirements, and geographic locations of users. The following descriptions of versions and description of deployment examples will help you better understand what solution fits your needs.
The desktop edition is a single user license available for PC's. Typically used by a scientist or statistician to analyze microarray data, conduct exploratory analysis, and develop new methods. The desktop edition gives you full access to the complete S-PLUSenvironment allowing for more individual control over your analysis options.
The desktopversion also works in concert with the Enterprise edition as a development system and prototyping environment.
S+ ArrayAnalyzer Network
The enterprise solution is licensed by CPU. Based on S-PLUSServer, the Enterprise edition is designed to be extensible and easy to integrate. The easy to use web based interface jump starts your analysis by providing you with out of the box access to rigorous statistical analysis. Using the included development tools you can customize your interface helping you to expand to meet new needs or enforce best practices.
S+ArrayAnalyzer enterprise solution can also serve as an engine for automated analysis that can be easily integrated with existing tools or databases. It can also integrate with other popular software packages like Spotfire Decision Site.
The CPU based license model makes it easy to deploy to many users simultaneously or run many automated batch processes at a time without ever running out of licenses, and is the most flexible of all deployments.
Feature List
Improved Study Design
- Reduce costs and bring drugs to market sooner with sequential studies
- Interactive interface makes it easy to explore tradeoffs between designs
Comprehensive Design and Evaluation Tools
- Sequential designs in unified family of Kittelson and Emerson, including all commonly used group sequential designs
- Family of designs based on error spending function of Lan and DeMets
- Comprehensive evaluation tools, including power, conditional power, sample size distribution, inference at the boundaries, and Bayesian analyses
Completely Extensible
- Powerful S language for extending functionality to fit your needs; from small to large projects, from simple to complex analyses
Flexible Monitoring Methods
- Implementation of stopping rules based on error spending functions or constrained boundaries
Easily Analyze and Interpret Your Results
- Exact p-values
- Exact confidence intervals
- Bias adjusted estimates of treatment effect
Easy to Learn
- State-of-the art GUI (Graphical User Interface)
- Documentation designed for the clinical trialist
- Fully integrated with S-PLUSdata analysis software
Advanced Visualization Tools
- Comprehensive set of specialized plots for designing studies, including power curves, ASN (average sample size) curves, and stopping probabilities
- Trellis graphics for powerful and effective comparison of designs
Powerful Validation Techniques
- Results validated against standard designs in the group sequential literature
It is not unusual for quantitative analysts to spend 90% of their time preparing financial data for analysis - leaving only 10% of their time for their core modeling and research responsibility. SunGard® and Insightful® partners have solved this problem with FAME S+Connector. The solution combines SunGard's FAME® data management solution for managing high-volume time series data and Insightful's S-PLUSsoftware platform for statistical data analysis and predictive analytics.
Point-and-Click for All the Data You Need - With FAME S+Connector quantitative analysts can rapidly create powerful models for even the most complex financial instruments. It also, offers a reliable, integrated application that can adapt and grow as your requirements change. Also, analysts can dramatically accelerate the process of bringing accurate data from FAME into the powerful S-PLUSenvironment for advanced quantitative analysis. S-PLUSgives users point-and-click access to data manipulation, graphing and statistics. S-PLUSprovides an easy to use, graphical user interface founded upon its object oriented, award winning S® language, making it easy to manipulate and integrate financial content from FAME.
Keep Up with Fast-Changing Business Requirements - Mainframe legacy and popular spreadsheet applications often lack the flexibility and power needed for quantitative analysts to easily adapt modern statistical methods and fast-changing business requirements to quickly create new models. The award-winning S programming language of S-PLUScombined with FAME's object-oriented time series database means that analysts can increase productivity while responding quickly to demands from their line of business customers.
Greater Productivity with More Complex Data Needs - FAME S+Connector can help simplify object identification and retrieval through optional tools, such as SunGard's PowerData and Excel-based FAME populator. These tools offers greater productivity in working with objects from the FAME container.
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