JMP Genomics. Release Notes. Version 6.0

Transcription

1 JMP Genomics Version 6.0 Release Notes Creativity involves breaking out of established patterns in order to look at things in a different way. Edward de Bono JMP, A Business Unit of SAS SAS Campus Drive Cary, NC 27513

2 JMP Genomics Copyright 2012, SAS Institute Inc., Cary, NC, USA All rights reserved. Produced in the United States of America. Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR , Commercial Computer Software-Restricted Rights (June 1987). SAS Institute Inc., SAS Campus Drive, Cary, North Carolina JMP, SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are registered trademarks or trademarks of their respective companies.

3 JMP Genomics, Version Release Notes This document describes changes and enhancements from JMP Genomics, Version 5.1 to JMP Genomics, Version 6.0. Processes are described in the order in which they first appear in the JMP Genomics menu 1. General Features JMP and SAS Platform Updates JMP Genomics 6.0 is built on the latest JMP major release, JMP For more information about the updates to JMP software that are included in this release, please see the JMP 10 Release Notes. JMP Genomics 6.0 is built on the latest SAS release, SAS 9.3. For more information about the enhancements to SAS analytical software that are included in this release, please see the SAS 9.3 Release Notes**. Full installation instructions will be available on the JMP Web site. Please note the following when you install JMP Genomics 6.0: You must uninstall JMP Genomics 5.1 and all SAS 9.2 components first using either the Add or Remove Programs (Windows 7) or the Uninstall a program utility from the Control Panel. If you have already installed SAS 9.3 on your computer before installing JMP Genomics, you might need to run the install twice. Initially, SAS will be updated, and when that installation has completed, you can re-start the installer again to install JMP Genomics. After installing, you will not see a desktop icon for JMP Genomics 6.0. If you want to create one, you can do so by browsing to the location of the JMP executable (typically C:\Program Files\SASHome\JMP\10) and creating a new shortcut to this file on your 1. Note: If you have a suggestion, comment, or encounter a bug in JMP Genomics 6.0, please click Send a Comment or a Feature Request under Genomics > Documentation and Help, or details to genomics@jmp.com. For bugs, it is especially helpful if you can attach a settings file for the JMP Genomics process in which you encountered the problem, along with a subset of your data that can be used to reproduce the error. If you cannot share a subset of your own data, but can reproduce the problem with one of our sample data sets, please send us a settings file for this so that we can replicate the error. We will make every effort to address the issue promptly. Thank you for taking the time to do this!

4 2 JMP Genomics, Version Release Notes desktop. To change the icon used for the shortcut on Windows 7, right-click on the shortcut and choose Properties. Click Change Icon and browse to the JMP Genomics icon in C:\Program Files\SASHome\JMP\10\LifeSciences\Documentation\Icons\ JMPGenomicsApp.ico. Viewing Results Generated Using Prior Versions of JMP Genomics in JMP Genomics A script is available from JMP Technical Support to modify results scripts and drill-down buttons created in earlier versions of JMP Genomics so that they can be run in JMP Genomics 6.0. Software Documentation Updates The JMP Genomics User Guide and Help System has been extensively redesigned. The User Guide is now online and is updated on a regular basis. Click the User Guide button, located at the bottom of every dialog, to open the online User Guide that provides details about every individual process, parameter and type of output available in JMP Genomics. Click the? buttons, located on the Genomics Starter to access information about selected categories, subcategories, and processes. Click the Process Description link at the top of every process dialog to open an HTML description of the process. Descriptions include a brief synopsis of what the process does, the data sets and other information needed to run the process, and links to detailed descriptions of the process output and how to interpret it. Click the Output Description link at the top of each Results window to open an HTML page containing detailed descriptions of the output data sets, graphics and drill-down options. Click the? button, located next to each parameter in the dialogs, to open an HTML page containing comprehensive information about what the parameter does, how to specify the parameter, and the types and functions of all available options. Viewing SAS Names and SAS Labels When JMP Genomics opens a SAS data set, the SAS labels are shown by default. In some cases, it can be useful to be able to change this so that the SAS names are visible instead. New menu items have been added under the JMP View menu to enable you to toggle back and forth between SAS Names and SAS Labels in open SAS data sets.. All Processes JMP Genomics now enables you to define studies. Accordingly, an option for specifying a Study has been added to all process dialogs. Use this drop-down menu to specify the data sets and settings associated with a previously defined Study. 2. Unfortunately, due to the extensive changes to the platform, we cannot guarantee that all results scripts generated in previous versions run correctly in JMP Genomics 6.0. Please contact technical support if you need assistance converting a script.

5 JMP Genomics, Version Release Notes 3 Studies A Study consists of specified files, data sets, metadata settings, and JMP scripts associated with a particular study or project. Once defined, studies greatly simplify identifying and locating input data sets and settings for any Analytical Process (AP), even if those files are saved to different directories. When you run a process with a study selected, that study is automatically updated to include the new output data sets created by the process and the setting used to create them. A metadata folder for each study gives a central location for tracking all the processes that have been run. Add Study New! This process enables you to set up a new study. Use it to identify all of the data sets associated with the Study, and specify the directory where all of the output from processes using this Study are to be stored. After a Study has been defined and selected as the currently active study, browsing for an input data set or a setting to load is greatly simplified so that dialogs initially display all the data sets/settings associated with the Study, even if they are saved to different folders. Manage Studies New! This process enables you to rename a Study, add new data sets and files to the study (even if these files are located in different directories), combine the study with additional studies, and delete all or part of a Study. View Study Metadata New! This process enables you to view the metadata, including data sets, settings and process logs, for any defined Study. JMP Genomics, Version Release Notes Note: You can also add a study using any of the JMP Genomics Import processes. Import and Experimental Design All Import Processes A New Study parameter has been added to all input engines, allowing you to create a new study with a name of your choosing that will contain the new data sets created by the input engine. This parameter is disabled if an existing Study is selected. Affymetrix Cytogenetics CHP (CYCHP) Input Engine This import engine can now import files generated from the new CytoScanHD array. Affymetrix Cytogenetics CEL Input Engine This import engine can now import files generated from the new CytoScanHD array.

6 4 JMP Genomics, Version Release Notes Plink Binary File Input Engine New! This new process reads a BED (binary PED) file, which encodes up to four genotypes (2 bits per genotype). This process requires two additional files: a BIM file which includes the names of the alleles, and a FAM file which includes phenotype information for observations. This input engine reads the files byte by byte and outputs a wide genotype data set and annotation map data set. This process also generates a Launch Follow-Up Process pane with a button for launching the Basic Genetics Workflow. Generate Counts from SAM (SAM Input Engine) Performance of this process has been significantly improved due to a new implementation of the counting algorithm. A new option has been added to the General tab that enables you to specify a Feature Identifier (if one is present in your SAM files) that can be used to summarize counts. This input engine can now import paired-end reads. Several new columns have been added to the design file output from this process: TotalReads, TotalCounts and CountPerRead. These columns include statistics calculated during import of SAM files for each sample. New buttons have been added to the Launch Follow-Up Processes pane of the output tabbed report. These buttons enable you to auto-launch the RPM Scaling and/or Upper Quartile Scaling processes after generating counts from your SAM files. Generate Counts from BAM (BAM Input Engine) Performance of this process has been significantly improved due to a new implementation of the counting algorithm. A new option has been added to the General tab that enables you to specify a Feature Identifier (if one is present in your BAM files) that can be used to summarize counts. This input engine can now import paired-end reads. Several new columns have been added to the design file output from this process: TotalReads, TotalCounts and CountPerRead. These columns include statistics calculated during import of BAM files for each sample. New buttons have been added to the Launch Follow-Up Processes pane of the output tabbed report. These buttons enable you to auto-launch the RPM Scaling and/or Upper Quartile Scaling processes after generating counts from your BAM files. Generate Counts from Eland (Eland Input Engine) Several new columns have been added to the design file output from this process: TotalReads, TotalCounts and CountPerRead. These columns include statistics calculated during import of Eland files for each sample.

7 JMP Genomics, Version Release Notes 5 Gene Model Summary A new radio button enabling you to select summarization at the exon or super-gene(s) level has been added. (Previously only exon-level summarization was available.) A new Include Intron Bins check box option has been added. This box should not be checked if you want to ignore introns when determining counts for features. CLC Bio Input Engine A new Create separate data set(s) for each chromosome when there are more variants than... option has been added. Use this option to specify the desired number of variants to set the maximum size of the tall and, if selected, wide output data sets. If the number of variants exceeds the threshold set, multiple data sets will be created. Complete Genomics Input Engine The performance of this import process has been greatly improved. A new ASM Files from version of Assembly Pipeline prior to 2.0 check box has been added to help accommodate changes for versions prior to 2.0 of Complete Genomics files. You can now select either a testvariants file created by CGA Tools or a Folder of Complete Genomics Files (containing a collection of previously supported files: var, MasterVarBeta, gene, dbsnpannotated, genevarsummary). Specifying a testvariants file grays out all folder-related options. Please note that working with testvariants files (which summarize across all samples) is much simpler than working with folders of files. Therefore, we recommend that you run CGA tools (downloadable from to create the single testvariants file whenever possible. A new Only Include SNP Variants check box is enabled on the Options tab when importing a testvariants file. Check this box to include only the SNP variants in the generated data set. If this box is not checked all variants including SNPs, substitutions, deletions, and insertions, are included. A new Create separate data set(s) for each chromosome when there are more variants than... option has been added. Use this option to specify the desired number of variants to set the maximum size of the tall and, if selected, wide output data sets. If the number of variants exceeds the threshold set, multiple data sets will be created. A new check box enabling you to import Complete Genomics MasterVar files has been added. JMP Genomics, Version Release Notes VCF Input Engine New options have been added for filtering genotypes based on GQ and DP scores in the VCF files being imported. You can now set individual genotypes to missing using these cutoffs, and specify a missing value threshold for excluding SNPs from the output data set created by this process. A new Create separate data set(s) for each chromosome when there are more variants than... option has been added. Use this option to specify the desired number of variants to set

8 6 JMP Genomics, Version Release Notes the maximum size of the tall and, if selected, wide output data sets. If the number of variants exceeds the threshold set, multiple data sets will be created. Workflows Basic RNA-Seq Workflow New! RPM Scaling and Upper Quartile Scaling have been added as normalization options. Genetics Rare Variant Workflow The Sequence Kernel Association Test (SKAT) has been added. Related options, including Variant Weights, Kernel Function, and Alpha and Beta values for Beta distribution, have been added. For more information about SKAT, please see Wu, M.C. et al. (2011) 3. Rare-variant association testing for sequencing data with the sequence kernel association test. American Journal of Human Genetics 89: For additional discussion of the method used to calculate SKAT p-values, please see: Liu, H., Y. Tang, and H.H. Zhang. (2009) 4. Genetics Q-K Analysis Workflow A new Compute Q variables from PCA check box has been added; uncheck this box to skip the PCA for computing the Q variables. You might do this, for example, if you have already generated your own Q variables. A new Perform multiallelic analysis on multiallelic markers check box has been added. Check this option to run the Expand Multiallelic Genotypes process and to create the Q and K matrices using the expanded genotypes. The workflow will then run the Marker-Trait Association process (instead of Q-K Mixed Model) using these matrices and the original genotypes with all alleles included in the model simultaneously. Note: In previous versions, and when this new option is not checked, multiallelic markers were/are converted to biallelic markers before performing any calculations or analysis. A new Filter to Select Markers for Computing the K Matrix parameter has been added. When specified, this parameter is used when the Relationship Matrix process is run in the 3. Wu, M.C. et al. (2011). Rare-variant association testing for sequencing data with the sequence kernel association test. American Journal of Human Genetics 89: Liu, H., Y. Tang, and H.H. Zhang. (2009). A new chi-square approximation to the distribution of nonnegative definite quadratic forms in non-central normal variables. Computational Statistics and Data Analysis 53: (

9 JMP Genomics, Version Release Notes 7 workflow. If this parameter is left unspecified, the Filter to Include Markers is used in Relationship Matrix instead. The Filter to Include SNPs for PCA option has been renamed to Filter to Include Markers for PCA. A new Pedigree ID Variable parameter has been added. If specified, this identifier will be used in the Relationship Matrix process. Note: This variable cannot be specified if either the Compute Q variables from PCA option or the Compress the K Matrix option is checked. To save computation time, a new "Fix covariance parameters" check box has been added. When this box is checked, the covariance parameter for the K Matrix is estimated one time only without the genotypes in the model and that estimate is used in each of the models. Linkage Mapping Workflow New! The Basic Linkage Mapping Workflow process simplifies the process of creating a genetic linkage map using marker genotypes for individuals or lines from an experimental inbred population. This automated workflow runs the Recombination and Linkage Groups, Linkage Map Order, Linkage Map Viewer, and Subset and Reorder Genetic Data processes. This process enables you to browse the workflow results for detailed information about the linkage groups identified and the order of markers within those linkage groups. You can view and export tables or graphics of your new marker map, and proceed to QTL analysis with a reordered version of your genotype data set that matches the new linkage map. Workflow Builder Options for running settings for Cross Validation Model Comparison, Learning Curve Model Comparison, and Test Set Model Comparison have been added. This update makes it possible for you to use the Workflow Builder interface to run multiple settings for these processes in one run. The ability to sort settings on the right side of the panel by AP name, settings name and time has been added. Selecting the same option twice will sort the settings in ascending and, then, descending order. JMP Genomics, Version Release Notes Genetics All Genetic Processes All marker column names are checked for a mismatch with variable from the annotation data set specified in the Marker Names Variable field (previously only the first 100 were checked). If one or more mismatches are detected, the error message will direct you to the line where the first mismatch occurs.

10 8 JMP Genomics, Version Release Notes Genetics Utilities Subset and Reorder Genetic Data A new action button enabling you to view excluded markers has been added. This button is included on the Summary Results tab (which displays the number of markers in the original data sets and in the output data sets) when filter criteria are specified in the Filter to Include Markers field. Recode Genotypes An option to allow selection of which allele (A or B allele) to code in terms of when Single-Character Homozygous Genotypes marker variable format is used, has been added. Note: This option is ignored if an Annotation Major Allele Variable is specified. Expand Multiallelic Genotypes New! This new process takes a multiallelic marker or SSR genotype coded within one column and expands the genotype out into separate marker columns for each allele with an option for either dominant coding (0 or 1 if present) or heterozygous coding (0,1,2). It also creates a corresponding annotation data set. This data sets generated by this process can be used in both the Relationship Matrix and Principal Components Analysis processes to create output data sets for use in Q-K analyses. Relatedness Measures Relationship Matrix Two new parameters, Annotation SAS Data Set and Filter to Include Markers have been added. Note: When an SVD root is calculated, the output data set will contain all the original marker variables, and the Q-K Mixed Model process will use all original marker variables. Population Measures A reference group can now be specified for calculating pairwise Fst values. This can help reduce computational time when there are a large number of groups to compare and you are interested only in comparisons relative to a single reference group.

11 JMP Genomics, Version Release Notes 9 Genetic Marker Statistics Marker Properties The P-Value Plots tab has been renamed HWE P-Value Plots. The Test for HWE parameter now includes an option for selecting None. All parameters on the P-Value Plots tab are disabled when None is selected, and while HWE p-values are still included in the output data set, no plots are created in the output tabbed report. A new Create Subset Genotype and Annotation Data Sets action button has been added to Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. Missing Genotype by Trait Summary A new Create Subset Genotype and Annotation Data Sets action button has been added to the Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. Linkage Disequilibrium GWAS Testing The LD Decay plot has been updated to use the JMP Bivariate platform instead of Overlay Plot. This update allows for assessment of the fit of the decay. JMP Genomics, Version Release Notes Case-Control Association A new Create Subset Genotype and Annotation Data Sets action button has been added to the Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. A drill-down option for plotting the trait by genotype for markers selected in the output plots has been added to the Results window. Box plots and points representing individual trait values are shown for each genotype group. Note: This Action Button is included only when input genotypes are in numeric format. PCA for Population Stratification A new option for using EigenCorr scores to select principal components to include in the regression when a single trait is specified has been added. Select this option to calculate the p-value for testing for nonzero correlation between each principal component (PC) and the trait variable and select a multiple testing method and alpha level for including a PC. Logistic regression is now used when the trait is binary. Additional plots and output data sets created include odds ratio plots (when the new Calculate trend odds ratios check box is

12 10 JMP Genomics, Version Release Notes checked), a volcano plot for the estimate of minor allele genotype effect and its p-value, and a data set containing all parameter estimates for each SNP model. A new option, Variables to Keep in PCA Data Set, enables you to specify those variables in the input data set that you want to retain in the output PCA data set. A new Create Subset Genotype and Annotation Data Sets action button has been added to Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. A drill-down option for plotting the trait by genotype for markers selected in the output plots has been added to the Results window. Box plots and points representing individual trait values for are shown for each genotype group. Note: This Action Button is included only when input genotypes are in numeric format. SNP-Trait Association A new Survival option that creates an output data set containing the survival function estimates has been added. The resulting data set can then be used to plot survival curves for selected SNPs. This feature is disabled when either By Variables or Interaction Effects are selected, or when more than one trait is specified. One action button and output data set is generated per test selected (Genotype/Trend). The Genotype test can now be used for Survival traits. If the Output genotype LS means and diffs check box is checked, the Volcano Plots tab on the Results window now includes volcano plots for the genotype LS diffs (2-0 and 1-0). Both differences and their p-values are also included in the main p-value data set. A new Create Subset Genotype and Annotation Data Sets action button has been added to the Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. A drill-down option for plotting the trait by genotype for markers selected in the output plots has been added to the Results window. Box plots and points representing individual trait values are shown for each genotype group. Note: This Action Button is included only when input genotypes are in numeric format. A column called MajorAllele is added to the p-value output data set when an Annotation Major Allele is not specified and numeric genotypes are not used. Survey SNP-Trait Association A new Create Subset Genotype and Annotation Data Sets action button has been added to the Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. A drill-down option for plotting the trait by genotype for markers selected in the output plots has been added to the Results window. Box plots and points representing individual trait val-

13 JMP Genomics, Version Release Notes 11 ues are shown for each genotype group. Note: This Action Button is included only when input genotypes are in numeric format. A column called MajorAllele is added to the p-value output data set when an Annotation Major Allele is not specified and numeric genotypes are not used. Quantitative TDT TDT A new Create Subset Genotype and Annotation Data Sets action button has been added to the Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. A new Create Subset Genotype and Annotation Data Sets action button has been added to the Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. GWAS Meta-Analysis A new Inverse variance - random effects model method has been added. The previously available Inverse Variance method has been renamed Inverse variance - fixed effect model. In addition, the previously named square root of sample size has been renamed P-values weighted by square root of sample size. When either of the inverse variance methods is selected, a volcano plot displaying P-value versus Overall Effect is created. In addition, an Action Button for generating Forest plots is surfaced. When either of the inverse variance methods is selected, a Heterogeneity Statistics tab displays distributions of Cochran's Q statistic and its p-value and I 2 values. JMP Genomics, Version Release Notes Other Association Testing Multiple SNP-Trait Association The Sequence Kernel Association Test (SKAT) has been added. Related options, including Variant Weights, Kernel Function, and Alpha and Beta values for Beta distribution, have been added. For more information about SKAT, please see Wu, M.C. et al. (2011) 5. Rare-variant association testing for sequencing data with the sequence kernel association test. American Journal of Human Genetics 89: For additional discussion of the method used to calculate SKAT p-values, please see: Liu, H., Y. Tang, and H.H. Zhang. (2009) Wu, M.C. et al. (2011). Rare-variant association testing for sequencing data with the sequence kernel association test. American Journal of Human Genetics 89:

14 12 JMP Genomics, Version Release Notes Marker-Trait Association K Matrix options (similar to those already found in Q-K Mixed Model) have been added. These options include Pedigree ID Variable, K Matrix Square Root Variables, List-Style Specification of K Matrix Square Root Variables, K Matrix is compressed, and Use lower boundary constraint for K matrix covariance. These options make it easier to perform association tests for multiallelic markers while correcting for relatedness. A new Fix covariance parameters check box has been added. When this box is checked, estimates of the covariance parameter for the K Matrix and any other specified random effects are computed one time without the genotypes in the model. The AP then uses those estimates in each of the models to save computation time. A new check box enabling you to use dominant coding for the trend test has been added. A new Create Subset Genotype and Annotation Data Sets action button has been added to the Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. Additional output data sets containing fixed and random effect estimates are now generated. SNP-SNP Interactions If the Output genotype LS means and diffs check box is checked, both differences and their p-values are also included in the main p-value data set. Q-K Mixed Model A new Fix covariance parameters check box has been added. When this box is checked, estimates of the covariance parameter for the K Matrix and any other specified random effects are computed one time without the genotypes in the model. The AP then uses those estimates in each of the models to save computation time. If the Output genotype LS means and diffs check box is checked, the Volcano Plots tab on the Results window now includes volcano plots for the genotype LS diffs (2-0 and 1-0). Both differences and their p-values are also included in the main p-value data set. A new Create Subset Genotype and Annotation Data Sets action button has been added to the Results window. This option creates subset genotype and annotation data sets of markers selected in the plots or output tables. This button is disabled if multiple Trait Variables, By Variables, or Interaction Effects are specified. A drill-down option for plotting the trait by genotype for markers selected in the output plots has been added to the Results window. Box plots and points representing individual trait val- 6. Liu, H., Y. Tang, and H.H. Zhang. (2009). A new chi-square approximation to the distribution of nonnegative definite quadratic forms in non-central normal variables. Computational Statistics and Data Analysis 53: (

15 JMP Genomics, Version Release Notes 13 ues for are shown for each genotype group. Note: This Action Button is included only when input genotypes are in numeric format. A column called MajorAllele is added to the p-value output data set when an Annotation Major Allele is not specified and numeric genotypes are not used. Haplotype Analysis Haplotype Estimation A new check box, available on the Output tab under the Phase Assignment Data Set Options section, enables you to output only the most probable haplotype pair to the output data set. The haplotype-trait association results, which were previously displayed only in the HTML report, are now collected in a new output table with the file suffix _hta. Haplotype Trend Regression A new Create HTML Output check box on the output tab enables you to view the SAS HTML output from PROC Haplotype. The regression results, which were previously displayed only in the HTML report, are now collected in a new output table. A table summarizing global tests has file suffix _htg, while a separate table of single tests is output with the suffix _hrs. This process now accommodates Class covariates. Linkage Maps and QTL JMP Genomics, Version Release Notes Recombination and Linkage Groups The parameters specified on the Analysis tab have been divided between the new Recombination and Linkage Groups tabs. The Recombination tab contains many of the parameter specifications found on the previous analysis tab in addition to a new option for specifying a multiple test method for the segregation tests. The Linkage Groups tab contains expanded clustering and grouping options. A new Framework Map tab has been added, enabling you to specify a framework map data set and associated variables.

16 14 JMP Genomics, Version Release Notes Linkage Map Order JMP Genomics 6.0 supports the Map Order Optimization option without the need to license SAS/OR separately. This process has been updated to exclude markers that are not closely linked to other markers from the ordering process. When such markers are excluded, a note is added to the output and an excluded marker data set is created. An Option for specifying a framework order variable from either the input data set or an order data set has been added. A new Genotype Data Set tab enabling you to specify an input SNP genotype data set and marker specifications has been added. Please note that this genotype data set field does not support data sets in Allele Format. A new option to Display Marker Genotype Cell Color Plots has been added. When this option is checked, markers ordered in linkage groups are displayed in genotype cell plots to assist in identifying mis-scored markers. Options for automatically breaking linkage groups, based on genetic distance or recombination fraction, have been added. An Action Button enabling you to reverse the order of markers in a linkage group when the marker order is correct, but the top markers are located at the bottom of the linkage group and vice versa, has been added to the output Results window. This button also reverses the order of interactive triangle plots that visualize details of recombination rates between markers. Linkage Map Viewer Two new parameters enabling you to italicize marker names in linkage maps have been added. You must specify a variable and the value of that variable that denotes a marker with a name that should be italicized. A new Maximum Number of Chromosomes Per Row in 3D Display parameter has been added. This option enables you to specify the number of chromosomes to be displayed on each line of the linkage map graphic. Single Marker Analysis An alpha-level parameter has been added. This option enables you to change the threshold line in output graphics. IM and CIM Analysis You can now select from two different QTL Mapping Model Algorithms: the EM Algorithm previously available and a new Haley-Knott Regression option. The Haley-Knott Regression enables you to specify both the number of permutations to perform and an alpha level for calculation of empirical LOD significance thresholds. A threshold parameter has been added to enable selection of a threshold LOD score. Columns for R 2 and N Obs for each trait are now included in the output data set.

17 JMP Genomics, Version Release Notes 15 Breeding Analysis GxE Interaction Multiple Environment Variables can now be specified. A new Filter to Include Observations field enabling subsetting of the input data set has been added. Copy Number Analysis Bin A new Bin Statistics parameter has been added to enable selection of a statistical criterion for setting bin size. Spectral Preprocessing 2D Bin A new Bin Statistics parameter has been added to enable selection of a statistical criterion for setting bin size. JMP Genomics, Version Release Notes Expression Quality Control Distribution Analysis An option to Display Standard Deviation versus Mean Plot has been added. When this option is selected, a new Cumulative Plots tab, appropriate for displaying count data, is surfaced on the output tabbed report. This check box is unchecked by default. Note: This tab might not be suitable for display of continuous expression data when negative values are present. A Density label has been added to the Y-axis of the kernel density plot.

18 16 JMP Genomics, Version Release Notes Correlation and Grouped Scatterplots A new Summary tab for summarizing grouped correlations has been added to the output tabbed report. All correlations are collected and run through the JMP One Way platform with the specified group variable used as the X-variable. This tab provides an overview plot that summarizes all pairwise correlations within each group. Normalization Loess Normalization This process has been updated to enable you to select a training subset based on missing status, that is used for scoring the rest of the data set. Check the Select Training Subset Based on Missing Status box on the Analysis tab to generate this subset and activate the following associated options: Percentage of Non-missing and Non-zero data to be in Training Subset. Use this option to specify a minimum percentage of nonmissing data used to determine which rows are included in the training subset. If a row contains too many missing values, it will be excluded from the training set. Rescale Data Based on the Total Measurements in Training Data Set. Use this option to rescale count data based on the total number of measurements included in the training set. Percentage of Data to be Trimmed before Summary. Use this option to trim values from the lower and upper ends of the distribution before summarizing and rescaling. Quantile Normalization This process has been updated to enable you to select a training subset based on missing status, that is used for scoring the rest of the data set. Check the Select Training Subset Based on Missing Status box on the Analysis tab to generate this subset and activate the following associated options: Percentage of Non-missing and Non-zero data to be in Training Subset. Use this option to specify a minimum percentage of nonmissing data used to determine which rows are included in the training subset. If a row contains too many missing values, it will be excluded from the training set. Rescale Data Based on the Total Measurements in Training Data Set. Use this option to rescale count data based on the total number of measurements included in the training set. Percentage of Data to be Trimmed before Summary. Use this option to trim values from the lower and upper ends of the distribution before summarizing and rescaling. Several methods 7 can now be used for quantifying the baseline across columns in the Input SAS Data Set (or the specified Baseline Reference SAS Data Set) for measurements with the same rank. Methods include Arithmetic Mean (default for quantile normalization), as well as

19 JMP Genomics, Version Release Notes 17 Geometric Mean, Median and Upper Quartile (which might be more appropriate for count data). An option enabling you to keep 0 values in the data set without normalizing them has been added. Normalization (Next-Gen Only) New! This new subcategory collects normalization 8 methods suitable only for count data, into one location. RPM Scaling New! This new process normalizes across count data for different samples by dividing the raw read counts by the total number of mapped reads, and multiplies the result by 1,000,000. For additional details about this calculation, please refer to Motameny et al. (2010) 9. A new data set of normalized RPM values is produced and a bar chart displays the RPM scaling factor for each sample. Upper Quartile Scaling New! This process applies a scaling factor that is based on the upper quartile value of each column. To calculate this scaling factor, the within column upper quartile is first calculated by excluding the rows of all 0 (or missing) values. Each upper quartile is then further standardized by dividing by the geometric mean of upper quartiles across columns to generate the upper quartile scaling factor for each column. TMM Normalization The output graphic now displays both the scaling factor for each sample and the total number of mapped reads by sample. JMP Genomics, Version Release Notes KDMM Normalization The output graphic now displays both the scaling factor for each sample and the total number of mapped reads by sample. 7. For more information about using these methods for mrna-seq data normalization, please see Bullard et al BMC Bioinformatics 11:94 ( 94) and the? Help for the Baseline Quantification option on the Options tab. 8. For more information about using these methods for mrna-seq data normalization, please see Bullard et al BMC Bioinformatics 11:94 ( 11/94) 9. Motameny S., Wolters S., Nürnberg P., Schumacher B Next Generation Sequencing of mirnas Strategies, Resources and Methods. Genes 1:70-84.

20 18 JMP Genomics, Version Release Notes Row-by-Row Modeling ANOVA The section on the Tests tab previously named X-Axis Filter has been renamed Mean Difference Filter. In this section, you can select a direction and magnitude which will be used as an additional filter when determining which results are called significant. A new option, Pseudo-Likelihood Convergence Criterion, is now provided on the Options tab for setting the desired convergence criteria for PROC GLIMMIX, which is run when count data is specified. Please note that PROC GLIMMIX might not be able to produce valid results for certain rows in your data set, even after adjusting the convergence criteria. When features/rows in your data set are found not to converge, these errors will now be ignored so that the analysis will complete. A Venn Diagram action button has been added to the Change Significance Criterion section of output tabbed report. Clicking this button enables you to select significance index variables to display in a Venn diagram. An option is available to choose proportional areas for the Venn diagram. A Create Subset with Mean Difference and P-Value Criteria action button now appears in the ANOVA tabbed report to enable you to create custom subsets of your ANOVA results. When this button is clicked, you can select one or more differences in the output data set and specify a cutoff difference value and -log 10 p-value to create a custom subset of genes that meet your criteria. Mixed Model Analysis A Venn Diagram action button has been added to the Change Significance Criterion section of the output tabbed report. Clicking this button enables you to select significance index variables to display in a Venn diagram. An option is available to choose proportional areas for the Venn diagram. One-Way ANOVA A Venn Diagram action button has been added to the Change Significance Criterion section of the output tabbed report. Clicking this button enables you to select significance index variables to display in a Venn diagram. An option is available to choose proportional areas for the Venn diagram. Pattern Discovery Hierarchical Clustering An option for imputing missing values for clustering has been added.

21 JMP Genomics, Version Release Notes 19 Multidimensional Scaling A Variables to Keep in Output field has been added. This option enables you to specify variables contained in the input data set that you want to retain in the output data set. P-Value Operations Meta-Analysis This process has been restructured to take as input a single data set. A new Inverse variance - random effects model method has been added. The previously available Inverse Variance method has been renamed Inverse variance - fixed effect model. When either of the inverse variance methods is selected and more than one effect is specified, a volcano plot displaying P-value versus Overall Effect is created. In addition, you can select points in the plot to drill-down to Forest Plots for each point. If only one effect is specified, a Forest Plot is displayed in the tabbed report. When either of the inverse variance methods is selected and more than one effect is specified, a Heterogeneity Statistics tab displays distributions of Cochran's Q statistic and its p-value and I 2 values. If only one effect is specified, the heterogeneity statistics are listed above the forest plot. Several parameters in this process have been renamed. The previously named Square Root of Sample Size has been renamed P-values weighted by square root of sample size. Other Effect Variables has been renamed Other Effect Estimate Variables, and Effect Direction or Size Variable has been renamed Effect Estimate or Direction Variable. An Effect ID variable has been added. This variable acts as a BY variable and enables you to work with multiple effects (for example, SNPs). JMP Genomics, Version Release Notes Genome Views JMP Genomics Browser A new Summary Statistic parameter that enables selection of statistics to calculate across the specified continuous variables, has been added to the Color Theme tab. A new button, Change Track Parameters, has been added to enable you to change which tracks are being displayed on a drill-down p-value plot.

22 20 JMP Genomics, Version Release Notes Annotation Analysis Get KEGG Identifiers This process has been updated to work with the new KEGG XML format that was deployed in December Get KEGG Pathways This process has been updated to work with the new KEGG XML format that was deployed in December A separate data set is created when retrieving a delimited list of pathways to which a given feature belongs. This data set, which contains a list of the pathways retrieved and their descriptions, can be merged with results data sets from enrichment analyses. Color KEGG Pathways This process has been updated to work with the new KEGG XML format that was deployed in December General Utilities Venn Diagram - Single Table Proportional areas are now available when comparing more than three groups. A Show Selected Rows button has been added. Use this feature to view the rows corresponding to the segments selected in the diagram. Venn Diagram - Multiple Tables Proportional areas are now available when comparing more than three groups.