new features in jmp® genomics 4 - sas...jmp genomics 3.2 introduced the ability to separate and...

WHITE PAPER

New Features in JMP® Genomics 4.1

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New Features iN JMP® GeNoMics 4.1

Table of Contents

Platform Updates ..............................................................................................2Expanded Operating Systems Support ...........................................................2New Import Features ........................................................................................3

Affymetrix Import and Analysis .................................................................. 3

New Expression Features .................................................................................5New Pattern Discovery Features .....................................................................8New Genetics Features .....................................................................................9

Enhancements to Multiple Genetics Processes ......................................... 9

New Support for Imputed Genotypes ...................................................... 10

New Annotation Features ...............................................................................13New Predictive Modeling Features ...............................................................16Documentation Updates .................................................................................18JMP Genomics Customizations .....................................................................19Conclusions .....................................................................................................19References .......................................................................................................19


JMP® Genomics software from SAS provides a suite of comprehensive tools for dynamic exploration and analysis of data from traditional microarray studies or summarized data from second-generation sequencing platforms. Its unique pedigree combines the JMP statistical discovery platform with industry-leading SAS® Analytics tailored for processing large genomics data sets. A point-and-click interface simplifies analysis workflows for all users. See and explore your data from every angle with automatically generated graphics, or select from a number of flexible platforms to create custom interactive plots.

This software solution helps biologists, biostatisticians and statistical geneticists around the world find patterns in data generated from genetics, expression, exon, miRNA, copy number, and proteomics studies. As your genomic studies expand to new areas or require the examination of multiple data types simultaneously, JMP Genomics lets you explore and make connections in a familiar environment – without wasting time and money learning multiple software packages and manipulating data sets to move between them.

As with previous releases, SAS developers relied heavily on suggestions and feedback from the global JMP Genomics user community to shape the feature set of JMP Genomics 4.1. These new and enhanced capabilities appeal to our existing user base, while also touching on new areas that are expected to draw attention from a wider group of potential users. As we plan for future releases, we invite you to share your own ideas for the product by e-mailing them to [email protected].

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mailto:[email protected]

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Platform Updates

JMP Genomics 4.1 adds many new features and is built on the latest JMP and SAS maintenance updates, JMP 8.0.2 and SAS 9.2M2. All current JMP Genomics license administrators should have received software download information for this new version automatically. If you are a license administrator and did not receive download instructions, please send an e-mail to [email protected].

Expanded Operating Systems Support

Earlier versions of JMP Genomics were supported only on 32-bit Windows operating systems. JMP Genomics 4.1 adds support for 64-bit Windows XP and 64-bit Vista Enterprise, Business and Ultimate editions1. JMP Genomics 4.1 64-Bit Edition is built on 64-bit versions of JMP and SAS and is capable of taking advantage of greater RAM memory resources under 64-bit operating systems.

JMP Genomics 4.1 has incorporated several features to help users submit larger jobs to remote SAS servers or SAS Grid Manager servers licensed with an appropriate mix of SAS products. For more details on the server SAS configurations necessary to run JMP Genomics in cooperation with other SAS products, please contact your SAS account executive.

1JMP 8.0.2 officially supports Windows 7. Some JMP Genomics users have installed JMP Genomics

4.0 on Windows 7 successfully. However, SAS 9.2 will not officially support Windows 7 until early 2010, and only production business edition versions of Windows 7 will be supported at that time. Since JMP Genomics 4.1 depends on underlying SAS components which are not currently supported on Windows 7, users who install JMP Genomics 4.1 on Windows 7 may experience unforeseen issues with installation or operation. We anticipate that JMP Genomics 5.0 will be fully tested and supported on Windows 7.

mailto:[email protected]


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New Import Features

JMP Genomics includes extensive support for import of common data types, including Affymetrix, Illumina and Agilent data. JMP Genomics supports import of full data tables or Final Report files for various Illumina data types. Data from custom arrays or Roche NimbleGen file formats also can be imported from single or multiple text files.

Affymetrix Import and Analysis

In addition to supporting legacy GCOS formats, JMP Genomics can import exon, whole transcript and 3’ expression CEL and CHP files from Affymetrix Command and Expression consoles. Import of Affymetrix SNP CEL, CHP, LOHCHP, CNCHP and CNAT files is also supported. JMP Genomics 4.0 could import exon CHP files through the Expression CHP import process, but release 4.1 adds an Exon CHP import process with defaults settings tailored for exon arrays.

In the Affymetrix SNP CEL import process, JMP Genomics has added the ability to perform base normalization only on autosomes to remove possible gender bias that could arise when gender ratios in experimental groups are unbalanced. If this option is selected, a two-step normalization procedure is applied. First, the observations from non-autosomes (by default, X and Y) are separated from autosomes. The normalization is then applied only to data for autosomes. Normalized data from the nearest smaller and nearest larger values from autosomes are used to interpolate a normalized value for each observation from a non-autosome.

Additionally, the new release enables the use of Affymetrix SNP6 MPS files to group probe sets into previously identified CNV regions. To use this option, specific SNP6 MPS files must be downloaded from the Affymetrix NetAffx Web site. This can be done within JMP Genomics using the Download NetAffx Files process found under Genomics > Import > Affymetrix > Download NetAffx Files. A new option allows automatic removal of AFFX control SNPs from output data sets.

CEL files from the Affymetrix Cytogenetics array can now be imported and normalized. Individual samples can be compared to a reference group using ANOVA. Copy number partitioning with fast circular binary segmentation (based on Venkataraman and Olshen, 2007) can be used to find breakpoints within individual samples and between samples using raw or normalized intensities or differences.

In response to user requests, JMP Genomics 4.1 offers new tools for the import of additional Affymetrix data types not supported in 4.0. Import of CEL and BAR files from Affymetrix tiling arrays is now supported. Quantile and MAT (Johnson et al 2006) normalizations are among the options available when importing tiling CEL files. While JMP Genomics 4.0 could import Affymetrix miRNA CEL files through the Expression CEL import process, a tailored import process for miRNA CEL files has been added for release 4.1. New Basic Workflows for tiling and miRNA are also available for downstream analysis of imported data.

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The Basic Tiling Workflow incorporates quality control options found in other JMP Genomics basic workflows, including analysis of data distributions, principal components and clustering patterns of samples. It also provides for integrated filtering based on the number of missing values, or other statistics such as mean, median, percentile and IQR. You can select additional normalization steps within the workflow, and quality control processes can be run before and after normalization for comparison. Integrated variance components analysis helps guide the selection of appropriate final models for One-Way ANOVA.

The Basic miRNA Workflow also offers a number of quality control options, including analysis of data distributions, principal components analysis on samples, variance components analysis, and scatter plots across replicate groups. Quality control analyses can be performed before and after normalization. Depending on the complexity of the specified model, you can elect to run either One-Way ANOVA or ANOVA.

Probe filtering was introduced for expression CEL files in JMP Genomics 4.0 to allow you to specify a list of probes to remove during import. Probes can be filtered prior to background correction, normalization, or summary. For example, you may wish to remove a list of probes with known SNP polymorphisms which could affect hybridization efficiency and potentially confound discovery of differences between experimental groups. This feature was frequently requested by users working with variant strains different from the one originally used to design an array, and with experimental data derived from multiple strains in the same experiment.

Probes can now also be filtered during import of miRNA and exon expression CEL files in release 4.1. The number of probes summarized is now reported in the output data set, and you can specify a minimum desired number of probes to be used to generate probe set-level values. Summaries not meeting the minimum are excluded from the output.

Users requested the ability to specify a set of reference quantiles to avoid repeating normalization when adding new samples to a study. The Loess Normalization and Quantile Normalization processes now permit normalization to a reference data set. In addition, this option is available when quantile normalization is selected during import of Affymetrix CEL file types.


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New Expression Features

JMP Genomics continues to enhance its tools for quality assessment and analysis of data from expression arrays and mRNA-seq experments. JMP Genomics 4.1 includes several new options for Correlation and Principal Components that are also incorporated into the expression, exon, copy number, and tiling workflows. Within the various pre-built expression workflows as well as the Basic Copy Number Workflow, it is now also possible to specify variance component effects on the Quality Control tab without running ANOVA modeling.

Examination of the contribution of different experimental factors to overall variance using variance components can be very useful during the quality assessment phase of an analysis, and the results can help guide final model selection. This approach involves first performing principal components analysis, then fitting a statistical model in which all effects are treated as random to assess the relative contribution of all experimental and technical sources to the overall variance captured by the assessed principal components. Users can now also specify a cumulative percentage of overall variance to explain through principal components analysis.

Variance components analysis can guide statistical model selection by revealing the overall weighted average proportion of variance due to experimental and technical variables. You can now specify a cumulative percentage of overall variance to explain through principal components analysis.

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JMP Genomics 4.1 greatly simplifies the process of determining which variance component effect or effects dominate the overall variance for each principal component. It adds automatic calculation of the proportion of variance explained by each variance component effect separately for each principal component, creating an interactive plot for all principal components assessed.

Visualize the relative contributions of experimental factors to the variance captured by each principal component.

JMP Genomics 3.2 introduced the ability to separate and journal results by chromosome in individual processes. Version 4.0 added this capability to selected basic workflows. With JMP Genomics 4.1, it is now also possible to separate results by chromosome within the Basic Exon Workflow by specifying chromosome as a grouping variable. When this option is selected, subset results data sets and figures are created for different chromosomes and surfaced as links in an output journal.

New action buttons also allow you to retrieve information on exon-level tests for selected effects from the component estimates table. This relieves the memory burden of working with the large component estimates tables generated for complex models. Currently, subsets are plotted using volcano plots to show differences for exons within a transcript, but multiple other graphics can be created using JMP graphical platforms, including overlay plots like the one in the following graphic.


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Plot p-values for exon-level tests to pinpoint which exons display significant evidence for alternative splicing. For more details, please see the white paper, “Alternative Splicing Analysis of Data from Affymetrix Exon Arrays Using JMP Genomics 4.1.”

The new JMP Genomics Difference Chooser now provides a simple way to customize which differences from an ANOVA analysis are included in the output data set. Difference Chooser also allows you to reverse the direction of computed mean differences, whose order was previously determined alphabetically.

Difference Chooser can be run as a stand-alone process or launched automatically from a button in a workflow or in the ANOVA dialog after specifying a model and difference set. By restricting the comparison set, you can reduce the number of tests that are used for applying multiple testing corrections.

The Difference Chooser allows you to select and/or reverse the direction for differences, save the list, and proceed with ANOVA modeling.

Support for new methods for p-value adjustment has been added in row-by-row modeling processes, as well as in predictive modeling methods, P-Value Browser, and enrichment processes. These include Adaptive Holm, Adaptive Hochberg, and both Adaptive and Dependent FDR.

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New Pattern Discovery Features

JMP Genomics has made considerable enhancements in recent releases, both visual and analytic, that enable users to correlate multiple genomics data types measured on the same samples, e.g., copy number and expression, SNP and expression, and other paired data types.

JMP Genomics 4.0 added a Cross-Correlation Matrix process to quickly screen for significant correlations between multiple numeric genomics data types measured on the same set of samples. This process is not limited to specific paired data types; it can calculate billions of pair-wise correlations between any two numeric biomarker measures on a desktop workstation. This process has been utilized to perform an eQTL screen correlating 12.5 billion pairs of numeric SNP and transcript-level intensities (Idaghdour et al, in press). The option to save only significant results at a specified significance level is available to reduce hard disk space requirements.

JMP Genomics 4.1 enhances the Cross-Correlation Matrix to incorporate annotation information for the second set of variables, as well as the first set, enabling greater flexibility in plotting results. In addition, new drill-down capabilities allow you to select correlations to plot original data values, and find all rows containing pair-wise correlations involving the selected variables.

Users requested ready-made tools for drilling down to raw intensities at the sample or group level to view interactive plots of intensity profiles. The new Plot Intensities process can be auto-launched on a selected subset of rows after ANOVA analysis, or run as a stand-alone process. Other numeric data types besides raw intensities can be examined with this process. The resulting plots can be further explored using the JMP Data Filter under Rows > Data Filter, which allows interactive selection of subsets based on any number of criteria in the results file.

Use Plot Intensities to visualize profiles across samples or within experimental groups, or use interactive Data Filter selection to easily identify interesting samples.


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New Genetics Features

JMP Genomics supports analysis of candidate gene and genome-wide genetic marker data sets. It offers an exceptional tool kit for the statistical geneticist: a wide range of simple and complex analysis options for large data sets, SNP and individual filtering capabilities, and support for genotypes from complex survey designs. New and experienced analysts alike can get started quickly with point-and-click genetic analysis processes. Analysts can even view the underlying SAS macros which call trusted procedures from SAS/Genetics® and SAS/STAT® behind the scenes.

Enhancements to Multiple Genetics Processes

JMP Genomics 4.1 adds to most genetics processes an option to verify that the marker order in the input data set matches the order of marker information in the annotation data set. You may now select an annotation column into the Marker Name field on the Annotation tab to perform this order check automatically. If mismatches are detected, you will be prompted to run the Subset and Reorder Genetics Data process to reorder the data sets properly.

Genetics processes that allow users to specify a variable that defines multiple annotation groups (e.g., chromosomes) and produce chromosome-position plots now automatically generate Manhattan plots. Results for individual chromosomes are still created in a journal. We have found that specifying single-digit numeric chromosome numbers in the format 01, 02…09 rather than 1,2,…9 produces the best labeling results for Manhattan plots.

View p-values from statistical tests individually by chromosome, or as multichromosome views, as in this Manhattan plot.

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A number of SNP filtering criteria are available in the Basic Genetics Workflow, including criteria based on minor allele frequency (MAF), percentage of missing genotypes, and Hardy-Weinberg Equilibrium (HWE). In response to user requests, release 4.1 adds the ability to control which individuals are used to compute the test for HWE in Marker Properties and Basic Genetics Workflow. You may now specify the trait value for a subset of samples to be used for HWE filtering (e.g., by specifying a trait value of “control”).

In Marker-Trait Association and SNP-Trait Association, support has now been added for specification of class fixed effects and interaction effects for survival traits. An option has been added to select the method used to calculate denominator degrees of freedom when a random effect is used. For SNP-Trait Association and Survey SNP-Trait Association, interaction effects are now allowed in the model for the trend test.

New Support for Imputed Genotypes

A number of users requested assistance with importing imputed genotypes generated in various other software programs. While initially used for predicting the genotypes of untyped SNPs in GWAS studies using SNP microarrays, imputation has recently gained favor as a useful tool for SNP analysis efforts in next-generation sequencing studies. Especially in cases where sequencing coverage is low, imputation can be used to strengthen genotype predictions.

As a result, JMP Genomics 4.1 now has the capability to import the wide data sets output from MACH and the tall format output from IMPUTE and BEAGLE. For assistance on determining how to import and analyze data sets from these programs, please consult the Imputed SNP Tutorial found under Genomics > Import > Other Genetics. Users also requested analysis options for performing association tests that could handle the uncertainty of imputed genotypes when performing genotype and trend tests for various trait types.

After import of an imputed genotype data set into JMP Genomics, two different output data sets are created. The genotype threshold output data set is a wide data set with one row per individual and one column per SNP and contains the most likely genotype for each individual at each SNP. This data set can be used as input to many existing JMP Genomics genetics processes if a user prefers to work only with the most likely genotype prediction for each SNP.

The genotype probabilities output data set is in stacked format grouped by SNP, with each row capturing one individual’s relative probabilities of each SNP genotype in separate columns. JMP Genomics has added a new analytic tool, Imputed SNP-Trait Association, to accommodate these imputed SNP genotype probabilities. Like SNP-Trait Association, this process can perform a general 2 degree of freedom test based on the probabilities of each genotype, or a regression testing for a linear trend of SNP alleles where the genotype uncertainty is taken into account when calculating the numeric representation of the SNP genotype.


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Features which create and use genetic relationship matrices – those that capture individual relatedness as well as population-level distance measures – also have been requested by many users. Population Measures was first introduced as Population Distance Matrix in version 4.0 to produce a population dissimilarity matrix. For JMP Genomics 4.1, Fst measures are also now calculated over all markers as well as for individual markers to quantify population differentiation.

Calculate and view Fst measures over all markers with Population Measures.

JMP Genomics 4.0 generated individual relatedness measures, including IBS (as the distance 1-IBS), using methods available in PROC DISTANCE. You could also create a kinship (K) matrix using pedigree information with the Kinship Matrix process, or import IBD (Identity By Descent) or relationship matrices output by other software for use in JMP Genomics.

In release 4.1, the Relationship Matrix process adds the capability to calculate genome-wide IBD, IBS, and Allele Sharing similarity measures between individuals directly from genetic marker information. This process can be used to create a kinship (K) matrix for a population where the exact pedigree structure is unknown, and to calculate the root of the K matrix via SVD.

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Visualize genetic distances between individuals using Relationship Matrix.

A K matrix can be further analyzed using Multidimensional Scaling in JMP Genomics. The square root of a K matrix can also be utilized in combination with a matrix of population structure information (Q) to correct association tests simultaneously for relatedness and population structure. This analysis could be performed in version 4.0 using the JMP Genomics Marker-Trait Association process by specification of the square root of the K matrix as a random effect.

The new JMP Genomics Q-K Mixed Model process simplifies the process by taking as input a Q matrix derived from the JMP Genomics PCA for Population Stratification process and the square root of a K matrix generated in Relationship Matrix or Kinship Matrix and using these matrices to correct association tests for both potentially confounding effects. Alternatively, other Q and K matrices can be imported into SAS data sets from external programs and used in this process.


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New Annotation Features

The JMP Genomics P-Value Browser, introduced in version 4.0, has been updated for JMP Genomics 4.1 in response to requests from users who wanted to view statistical results overlaid on genome representations within JMP Genomics. These enhancements add the option to create a custom genome view with p-value ticks overlaid.

Multiple p-values can be shown in a summary view within P-Value Browser to identify regions of shared significance.

P-Value Browser can utilize custom genome color themes created in the new Chromosome Color Theme process, or create genome color themes based on summarization over p-values within specified genomic intervals.

You also can display single chromosomes as circular in P-Value Browser, as shown here for a bacterial genome.

In the Chromosome Color Plot display, color bands can be selected to drill down to display detailed interactive p-value plots, and points selected to drill down further and plot SNP and gene tracks on statistical results. Interactive adjustment of the cutoff for showing p-values allows you to increase or decrease the number of p-value indicators shown, and more significant findings are darkened for easier identification.

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JMP Genomics 4.0 supported overlay of exon level gene tracks defined within text files. JMP Genomics 4.1 also adds support for overlay of SNP tracks on p-value plots. Both SNP and gene tracks can now be defined to allow download of annotation information on the fly from UCSC during drill-down. You can select a custom color and label for your tracks. An experimental process also allows creation of a gene track from a standard GFF file.

Settings files for automatically downloaded tracks and gene tracks defined by text files can be specified on any Tracks tab, such as the ones in P-Value Browser or ANOVA. Note that an active Internet connection must be present to use the automatically downloaded track information.

The interactive visualizations offered by P-Value Browser complement a number of existing JMP Genomics tools that help you integrate annotation information with statistical results. If you also license Ingenuity Pathways Analysis software, you can use the IPA Upload process to upload a gene list in JMP Genomics to IPA for further analysis. Using various other JMP Genomics Annotation tools, you can also perform enrichment analysis to identify over-represented functional categories, create Venn diagrams, retrieve and color KEGG pathways, and create custom tracks for IGB and UCSC.

JMP Genomics initially introduced the Venn Diagram process in release 3.2. Venn diagrams can be created using up to five columns of binary indicators, where 1 indicates membership in a category and 0 indicates non-membership. Indicators can be created to reflect statistically significant differences (e.g., those automatically generated by a process like ANOVA), annotation category membership, or you can create groups using other custom criteria. Indicators for custom groups can be created and named by selecting relevant rows and clicking Genomics > Annotation > Create 0-1 Indicator.

The Venn Diagram control window has been significantly enhanced in JMP Genomics 4.1, adding new controls that allow adjustment of the position of category labels, count labels, and circles. In addition, colors can be adjusted easily, circles can be resized on the fly, and circle outlines can be added or removed.

(Figure: Example Venn)

Customize Venn diagram colors, labels and circle sizes.


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A new Create Web Link process has been added to allow you to create an HTML report with embedded queries as links to a Web site. Links can be created using information within any open table, such as a table of significant changes from ANOVA containing merged annotation information. Additional variables from the open table can also be retained in the report (e.g., differences, p-values).

You can create reports using sample link settings, or create new custom links using knowledge of the query structure for any search page. An active Internet connection is required to create Web link reports.

Three new processes replace our previous KEGG functionality. You can send a list of genes to KEGG to retrieve a corresponding list of KEGG gene identifiers. Once the KEGG IDs have been retrieved, they can be used to add a new column to the original gene list containing pathway identifiers in delimited format for each gene. This information can then be used to perform enrichment analysis. Finally, pathways can be colored by expression values and viewed as Web pages.

Color KEGG pathways by expression values to identify sets of co-regulated genes.

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New Predictive Modeling Features

JMP Genomics introduced a row-by-row modeling survival analysis process in release 3.2. In JMP Genomics 4.1, a new Survival Predictive Modeling process also calls SAS PROC PHREG behind the scenes. It fits a Cox proportional hazards model for survival data, using a time-to-event variable and an optional censor indicator to estimate survival functions and median survival time. Forward, backward and stepwise model selection methods are available.

Display predicted survival curves for individuals (top image) and median survival estimates for groups (bottom image).


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ROC curves and ROC statistics are now surfaced in multiple predictive modeling processes for binary traits. ROC curves and statistics for individual models are available in Cross-Validation Model Comparison via drill-down on points representing individual models runs.

Display ROC curves (left) and ROC statistics (right) for individual predictive models.

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Documentation Updates

The extensive documentation within the JMP Genomics User Guide has been updated with 30 new chapters for processes introduced in JMP Genomics 4.1. Content in the existing 191 chapters has also been updated, with descriptions and examples that incorporate new features. All books of the JMP Genomics User Guide are now accessible from the menu, and a new User Guide button has been added to the bottom of all dialogs to launch the relevant documentation volume.

New JMP Genomics Tips of the Day highlight 36 useful tips and tricks for JMP Genomics users. A randomly selected JMP Genomics Tip of the Day will be shown automatically when JMP Genomics launches.

Tips of the Day provide valuable information and shortcuts for JMP Genomics users.

If you do not see the Tips of the Day, you may need to check the settings within your JMP Preferences (found under File > Preferences) to make sure that you have checked the box Show the Tip of the Day at startup.

If you do not wish to see the tips when JMP Genomics launches but would like to review them, you can access the tips by clicking on Help > JMP G Tip of the Day.


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JMP Genomics Customizations

JMP Genomics 4.0 introduced a number of enhancements for users wishing to customize their experience. You can now set your own custom default settings for individual JMP Genomics analysis processes and specify default folders for input, output, library files, and annotation. JMP Genomics 4.0 also first allowed you to elect to save in the output directory a copy of the SAS macros that ran during an analysis.

JMP Genomics 4.1 introduces the additional option to archive a copy of the resolved SAS macro code generated for each analysis. In addition, the new Load Genomics Setting utility located under the File menu simplifies the process of locating and loading existing JMP Genomics dialog settings. You can simply select any JMP Genomics setting from the file system to auto-load parameters in the correct JMP Genomics process window.

Conclusions

JMP Genomics has rapidly surpassed the statistical and visualization capabilities offered by competitors to become the analytic tool of choice for expression and genetics data generated from traditional microarray platforms or summarized from next-generation sequencing platforms. With more than 200 custom processes already built on trusted JMP and SAS software, the JMP Genomics development group continues to add capabilities to help researchers analyze new data types quickly and effectively. Organizations seeking to consolidate analytic toolkits across groups and leverage existing investments in JMP and SAS will find JMP Genomics an especially attractive option.

References

Johnson WE, Li W, Meyer CA, Gottardo R, Carroll JS, Brown M, Liu XS. Model-based analysis of tiling-arrays for ChIP-chip. Proc Natl Acad Sci U S A. 2006 Aug 15;103(33):12457-62. Epub 2006 Aug 8.

Venkatraman ES, Olshen AB. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics. 2007 Mar 15;23(6):657-63. Epub 2007 Jan 18.

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