JMP® Genomics: Building Blocks of Success
With every update of our all-in-one desktop software, JMP Genomics builds on the strength of its platform, which combines powerful SAS analytics and highly interactive JMP data visualization capabilities. Explore the expanded JMP Genomics Web site to learn about new features and enhancements introduced in JMP Genomics 4. These capabilities build on features that were included in earlier releases.
- Affymetrix Experimental Design Wizard
- Basic Workflows for Expression, Genetics, Copy Number and Exon Analysis
- Affymetrix CN CHP Input Engine
- Missing Genotype by Trait Summary
- Support for QTL Analysis
- Survival Analysis
- Learning Curves
- Kinship Matrix
- GEO Submission Tool
- Partial Correlation Diagram
- Data Stack
- RMA and GCRMA
- Genetics Processes
- Action Buttons for One-Way ANOVA, ANOVA and Mixed-Model Analysis
- Illumina Expression Input Engine
- Genetic Marker Properties.
- Copy number import tools for Affymetrix® CEL and CNAT files
- Copy number import tools for Illumina® BeadStudio files
- Copy number analysis workflows
- Affymetrix CHP Expression Wizard
- NetAffx™ download capabilities
- PCA for population stratification
- Filter intensities before analysis
- Additional predictive modeling processes
- Interactive Venn diagrams
- Significantly expanded documentation of individual features
- View Import Tutorial Journals within JMP Genomics
- Import data from Affymetrix Expression, Exon Expression and SNP GeneChips
- Import Illumina genotype and expression data
- Import wide text files into JMP Genomics
- Perform whole-genome association studies
- Take PCA data for a spin
- View sample information on hierarchical cluster dendrograms
- Build workflows with multiple JMP Genomics processes
- Compare the performance of multiple cross-validated predictive models
- Create custom contrasts for ANOVA
- Subset, reorder, and recode SNP data sets
- Create custom tracks for the UCSC Genome Browser and Affymetrix Integrated Genome Browser (IGB)
Affymetrix Experimental Design Wizard
Assists in creating a design file for a set of SNP or expression CEL or CHP files. This interactive wizard can create a design file from paired ARR and data files or an existing text or Excel file that lists names of the data files. After the design file is created, it is automatically loaded into the appropriate import process.
Basic Workflows for Expression, Genetics, Copy Number and Exon Analysis
Incorporates options from multiple processes in a simplified dialog and automatically builds a workflow behind the scenes using the JMP Genomics Workflow Builder. Workflows incorporate various processes, including quality control, filtering, normalization and modeling. Basic Exon Workflow automatically creates a model to test for alternative splicing.
Affymetrix CN CHP Input Engine
Imports and combines Affymetrix CN CHP files created by Affymetrix Genotyping Console Version 2 into a single SAS data set. The imported data set may be launched directly into the Basic Copy Number Workflow.
Missing Genotype by Trait Summary
Tests whether missing genotypes at a particular genetic marker are related to a trait. Use interactive plots of results to view differences in missing value patterns between cases and controls.
Support for QTL Analysis
An experimental QTL Mapping submenu resides in the main Genomics menu. In addition, a WinQTLCart Input Engine lets users import a Windows QTL Cartographer .MCD file into a SAS data set containing marker genotypes and traits, and a SAS data set containing annotation information. Processes for single marker analysis, combined interval mapping, and a utility for building QTL genotype probability data sets also are included.
Survival Analysis
Tests association of each row of the input data set with a censored response, fitting a Cox proportional hazards model on a row-by-row basis to a normalized data set.
Learning Curves
Employs cross-validation to evaluate a model using different sample sizes, thereby revealing the influence of sample size on accuracy and variability of the model. A wide variety of distance metrics are available for making the predictions.
Kinship Matrix
Creates a matrix containing either kinship coefficients or covariance coefficients between pairs of related individuals. These coefficients can then be used as random effects in order to analyze family data in an association setting, using processes (e.g., Marker-Trait Association, SNP-Trait Association) that can accommodate random effects.
GEO Submission Tool
Formats an experiment for submission to the Gene Expression Omnibus (GEO) database. Data is placed in MiniML format and written to an .XML file for batch submission to GEO.
Partial Correlation Diagram
Infers potential relationships between variables by plotting each one as a node and connecting the nodes with line segments.
Data Stack
Converts tall SAS data sets and information from an experimental design file into a singled stacked data set. This feature includes an option for subsetting the data, using the first n rows, for preliminary testing. This is particularly useful for SAS programmers who wish to convert a tall data set from JMP Genomics into a stacked format for SAS PROC MIXED.
RMA and GCRMA
JMP Genomics 3.2 introduced the option to perform GCRMA background correction on large sets of Expression CEL files. Performance of RMA normalization during CEL import also was significantly improved.
Genetics processes
Allows users to specify that all markers are biallelic and trigger a faster implementation of case-control association. A time-saving PCA Data Set option allows users to rerun PCA population stratification analyses without having to repeat the principal component calculations.
Action buttons for drilling down into results generated by one-way ANOVA, ANOVA and mixed-model analysis
Action buttons launched with the output of JMP Genomics ANOVA processes add the ability to subset the data in either wide or tall formats. Additional options allow users to construct one-way plots for selected rows and fit models to the input data for selected rows for more complex models.
Illumina expression input engine
Adds the ability to select columns to import, an option to apply Log2 transformation to intensities, options to filter intensities based on detection p-values, and options for annotation data output.
Genetic marker properties
Marker properties output includes interactive distributions of minor allele frequency (MAF plots) and missing genotype proportion plots. These plots may be generated for the entire data set or annotation group.
Copy number import tools for Affymetrix CEL and CNAT files
Import raw SNP intensities from Affymetrix SNP CEL files, for all mapping arrays, including Genome Wide Human 5.0 and 6.0. Normalization and summarization may be performed during CEL file import or post-import. Users may also import files containing processed copy number data output from CNAT 4.0 in the new CNAT Input Engine, or import data from previous versions of CNAT using text import tools.
Copy number import tools for Illumina BeadStudio files
Import copy number data from Illumina BeadStudio Final Report or Full Data Tables. BeadStudio users can also export sample information files from BeadStudio and automatically merge them with SNP or copy number data files during data import into JMP Genomics.
Copy number analysis workflows
Quality control tools assess intensity data from copy number data sets, including PCA and analysis of data distributions. One-way ANOVA analysis allows speedy assessment of group-level differences in copy number, for binned or probeset-level data. A more advanced workflow includes the bivariate one-way ANOVA, which uses information from SNP probes to allow simultaneous comparisons of copy number and allele frequency differences between experimental groups.
Affymetrix CHP Expression Wizard
This wizard streamlines import of expression data contained in Affymetrix CHP files by automatically creating a workflow for analyzing that data through a simplified, interactive interface. Highlights of the workflow include automatic import of design information from ARR files, text files, or an existing design file, import of expression CHP files, selection of important design variables, download of NetAffx annotation information, and optional upload of results to Ingenuity Pathway Analysis. Results are presented as links in a journal, and may be launched to review tables and graphics for each process included in the workflow.
NetAffx download capabilities
Download annotation, library, map, or other accessory files from NetAffx within JMP Genomics using a stand-alone NetAffx download tool or through the interactive Affymetrix Expression CHP Wizard. Log in to NetAffx, select an array for which to download files, and choose the desired files to download through an interactive dialog.
PCA for population stratification
In addition to offering a stand-alone PCA implementation which may be applied to whole-genome SNP data sets, JMP Genomics offers a SAS-based implementation of the EIGENSTRAT method, which allows the use of PCA to adjust for population stratification when conducting association tests. This feature was implemented in response to user requests to provide methods for adjusting for the potentially confounding effects of population structure in whole-genome association studies.
Filter intensities before data analysis
This process offers a number of options for replacing low and high intensity values in a data set by column, and removing rows from a data set that fail to meet user-defined performance criteria. For example, this process may be used to set low or high intensity values to missing, and to remove rows with a specified number of missing values, or with a mean, median, percentile, standard deviation, or inter-quartile range of a specified value.
Interactive Venn diagrams
Create up to five-way clickable Venn diagrams to compare significant gene lists generated by ANOVA, mixed model, one-way ANOVA, or bivariate one-way ANOVA, or to compare any custom lists generated during statistical or annotation analysis.
Additional predictive modeling processes
JMP Genomics 3.1 built on the advanced predictive model comparison capabilities introduced in the Cross-Validation Model Comparison process. The Test Set Model Comparison Process allows users to apply predefined predictive modeling settings to additional test sets to compare the performance of each model. The Distance Scoring process. also new for JMP Genomics 3.1, and existing predictive modeling processes were enhanced with new options for statistical filtering during predictor reduction.
Significantly expanded documentation of individual features
The JMP Genomics User Guide Supplement was greatly expanded for JMP Genomics 3.1 to include 41 chapters that describe in detail the use cases and options for JMP Genomics processes.
View Import Tutorial Journals within JMP Genomics
Bring data into JMP Genomics by following our Import Tutorials. Launched off a centralized import starter application, the tutorials feature step-by-step instructions on creating experimental design files, importing data and annotation information, and using embedded buttons to launch the relevant JMP Genomics dialogs. For JMP Genomics 3.1, import tutorials were updated, and new tutorials added for Affymetrix SNP CEL, Affymetrix CNAT, and Illumina Copy Number.
Import data from Affymetrix expression, exon expression and SNP GeneChips
JMP Genomics 3 was the first version certified as Affymetrix GeneChip compatible for expression, exon expression, and SNP analysis. The software supports import of CEL and CHP files generated from most Affymetrix arrays. Users can access expression data in GCOS- and AGCC-formatted Affymetrix CEL and CHP files, and import genotype CHP files for all Affymetrix SNP arrays. CEL files from Affymetrix SNP GeneChips, including large sets of Genome Wide Human SNP 5.0 and 6.0 GeneChips, can be imported for copy number analysis. JMP Genomics 3.1 offered significantly improved performance for import of large sets of CEL files and SNP CHP files. Our ARR File Parser compiles experimental information stored within sets of AGCC ARR files into a JMP Genomics experimental design file template. Users can perform RMA during CEL file import, export normalized expression data in CHP file format, and select library files for use with exon and whole-transcript arrays.
Import Illumina genotype and expression data
JMP Genomics can import expression, genotype, and copy number Final Report and Full Data Tables exported from Illumina BeadStudio. Beginning with JMP Genomics 3.1, sample information files and map files exported from BeadStudio could be automatically integrated with SNP and copy number data during import into JMP Genomics.
Import wide text files into JMP Genomics
Very wide text files up to 1 million columns could be accommodated beginning with release 3.1. Users may specify types and lengths of variables as desired, and performance was improved by allowing the user to select a subset of columns to scan to determine variable attributes.
Perform whole-genome association studies
The SNP-Trait Association process in JMP Genomics 3 supported very large whole-genome association studies. Offering a streamlined set of analysis choices, it built on the power of the Marker-Trait Association process but was optimized for whole-genome SNP analysis for up to a million SNPs for thousands of individuals. Additional genetics processes such as Marker Properties and Case-Control Association also were optimized for large whole-genome studies. JMP Genomics 3.1 added the Dominant, Recessive and MAX tests for association to Case Control Association, and enhanced the TDT process to use wide data sets. Also, numerous genetics processes were enhanced to use numeric as well as character genotypes.
Take PCA data for a spin
3-D graphics were new in JMP Genomics 3. Spinning principal component plots let users look at results from a different angle, and change the coverage, color and transparency of markers or contour ellipsoids to create customized output. JMP Genomics 3.1 offered principal components analysis for expression, whole-genome association data, and SNP intensity data.
View sample information on hierarchical cluster dendrograms
Combine statistical data with sample information by specifying grouping variables in the Hierarchical Clustering process to visualize sample information superimposed on the clustering dendrogram.
Build workflows with multiple JMP Genomics processes
Use the Workflow Builder interface to link sets of commonly used settings for JMP Genomics processes into streamlined workflows. This feature appeals to power users who have settled on a best practice workflow through JMP Genomics processes. A comprehensive workflow can automate multiple steps – data import, quality control, statistical analysis, modeling, annotation of results – and push the result scripts into links in a JMP Journal. Existing workflows may be saved, modified or streamlined as needed, and even pre-tested using a small subset of data.
Compare the performance of multiple cross-validated predictive models
Assess which statistical model is best suited for making predictions from your genomics data set. The Cross Validation Model Comparison process allows you to compare cross-validation statistics for an arbitrary collection of predictive models and determine which models are best suited for prediction from that particular data set.
Create custom contrasts for ANOVA
Create custom contrasts between important levels of experimental factors using the Estimate Builder process, which provides a menu-driven interface to create SAS Estimate statements to be used by the ANOVA and Mixed Model processes. Use this process to create custom hypothesis tests to assess the relative importance of specific combinations of fixed effects on gene expression.
Subset, reorder and recode SNP data sets
Use new genetics data utilities to subset and reorder SNP data, and recode between character and numeric formats.
Create custom tracks for the UCSC Genome Browser and Affymetrix Integrated Genome Browser (IGB)
View statistical data in genomic context using the UCSC Genome Browser or Integrated Genome Browser. Users may navigate to locations in the browser via a Web link table, and create a custom track containing data on a test statistic or p-value for upload and viewing in a browser.
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JMP Genomics 4 Capabilities and Features
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