JMP Genomics 3.1 New and Enhanced Features

Category Genomics>Gene Expression Analysis/Profiling/Tools

Abstract JMP Genomics is a statistical discovery software solution. It can be used to uncover meaningful patterns in high-throughput genetics, gene expression microarray and proteomics data. JMP Genomics delivers to the desktop the graphical and analytic capabilities required by scientists working with large genomics data sets. JMP Genomics provides biologists and biostatisticians with flexible, menu-driven platforms to access, evaluate, analyze and explore data interactively to discover biologically relevant patterns.

The ability to analyze copy number data is new in JMP Genomics 3.1, which also includes capabilities for analysis of microarray, Single Nucleotide Polymorphism (SNP) and proteomics data sets. This desktop analysis platform is designed to handle the enormous data sets common in genomics research. JMP Genomics 3.1 adds new capabilities to the JMP Genomics 3 platform, and enhances previously introduced features:

1) New! Copy number import tools for Affymetrix CEL (cell intensity) and Chromosome Copy Number Analysis Tool (CNAT) files -- JMP Genomics 3.1 adds the capability to 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.

2) New! Copy number import tools for Illumina BeadStudio files -- Import of copy number data from Illumina BeadStudio Final Report (see G6G Abstract Number 20002A) or Full Data Tables is supported in JMP Genomics 3.1. BeadStudio users may now also export sample information files from BeadStudio and automatically merge them with SNP or Copy Number data files during data import into JMP Genomics.

3) New! Copy number analysis workflows -- JMP Genomics offers a number of quality control tools to assess intensity data from copy number data sets, including principal component analysis (PCA), and analysis of data distributions. One-Way analysis of variance (ANOVA) analysis allows speedy assessment of group-level differences in copy number, for binned or probe set-level data. A more advanced workflow includes the new Bivariate One-Way ANOVA, which uses information from SNP probes to allow simultaneous comparisons of copy number and allele frequency differences between experimental groups.

4) New! Affymetrix CHP Expression Wizard -- This wizard streamlines import of expression data contained in Affymetrix summarized expression values (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 Amber Graphic (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 (see G6G Abstract Number 20017U). Results are presented as links in a journal, and may be launched to review tables and graphics for each process included in the workflow.

5) New! 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. You may log in to NetAffx, select an array for which to download files, and choose the desired files to download through an interactive dialog.

6) New! 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 3.1 also 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 provides methods for adjusting for the potentially confounding effects of population structure in whole genome association studies.

7) New! Filter intensities before data analysis -- This new 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.

8) New! 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.

9) New! Additional predictive modeling processes -- JMP Genomics 3.1 builds on the advanced predictive model comparison capabilities introduced in JMP Genomics 3.0 with the Cross Validation Model Comparison process. The Test Set Model Comparison Process allows users to apply pre-defined predictive modeling settings to additional test sets to compare the performance of each model. The new Distance Scoring process is also new for JMP Genomics 3.1, and existing predictive modeling processes have been enhanced with new options for statistical filtering during predictor reduction.

10) Enhanced! Significantly expanded documentation of individual features -- The JMP Genomics User Guide Supplement has been greatly expanded for this release to include 41 chapters which describe in detail the use cases and options for JMP Genomics processes.

11) Enhanced! View Import Tutorial Journals within JMP Genomics -- Bring data into JMP Genomics by following the 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 have been updated, and new tutorials added for Affymetrix SNP CEL, Affymetrix CNAT, and Illumina Copy Number.

12) Enhanced! Import data from Affymetrix Expression, Exon Expression and SNP GeneChips -- JMP Genomics 3 is Affymetrix GeneChip compatible for expression, exon expression, and SNP analysis. Software supports the import of CEL and CHP files generated from most Affymetrix arrays. Users may access expression data in GeneChip Operating Software (GCOS)- and Affymetrix GeneChip Command Console (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, may be imported for copy number analysis. JMP Genomics 3.1 offers 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 Robust Microarray Analysis (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.

13) Enhanced! Import Illumina genotype and expression data -- JMP Genomics can import expression, genotype, and now copy number Final Report and Full Data Tables exported from Illumina BeadStudio. In JMP Genomics 3.1, sample information files and map files exported from BeadStudio can be automatically integrated with SNP and Copy Number data during import into JMP Genomics.

14) Enhanced! Import wide text files into JMP Genomics -- Very wide text files up to 1 million columns can now be accommodated. Users may specify types and lengths of variables as desired, and performance has been improved by allowing the user to select a subset of columns to scan to determine variable attributes.

15) Enhanced! Perform whole-genome association studies -- The SNP- Trait Association process in JMP Genomics 3 supports very large whole-genome association studies. Offering a streamlined set of analysis choices, it builds on the power of the Marker-Trait Association process but has been 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 have also been optimized for large whole-genome studies. For JMP Genomics 3.1, the Dominant, Recessive and maximum test (MAX) tests for association have been added to Case Control Association, and the TDT process has been enhanced to use wide data sets. Also, numerous genetics processes have been enhanced to use numeric as well as character genotypes.

16) Enhanced! Take PCA data for a spin -- Bring a new dimension to your quality assessment and data visualization process with 3-D graphics new in JMP Genomics 3. Spin principal component plots to 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 offers principal components analysis (PCA) for expression, whole-genome association data, and SNP intensity data.

17) Enhanced! 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.

18) 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.

19) 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.

20) Create custom contrasts for ANOVA -- Create custom contrasts between important levels of experimental factors using the new Estimate Builder process, which provides users 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.

21) 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.

22) 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.

Note: For additional info and features for JMP Genomics 3.1 please click here.

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G6G Abstract Number 20007A1

G6G Manufacturer Number 102325