JMP Genomics 5 provides tools to help connect your high-dimensional genomics data sets to functional information so that you can quickly identify pathways, gene sets or loci that are significantly affected in experiments or patient populations. Examining higher-level patterns can improve signal detection when affected genes or SNPs vary from individual to individual.
In prior versions of JMP Genomics, we offered enrichment analysis using Fisher and PAGE tests. We have now added support for Cochran-Armitage trend tests into our Gene Set Enrichment process, and the software now allows you to set the number of bins for separating continuous significance variables into categories when performing tests. We’ve also added an option to specify the direction of significance variables to better accommodate various test statistics, from bi-directional t-statistics to raw and transformed p-values.
Our new Gene Set Scoring process implements an alternative approach to enrichment-style analysis, summarizing raw or normalized continuous data for each sample at the pathway level. This process enables you to compare individual samples either to an overall mean or to a group mean, and it produces a pathway-summarized data set that you can use to seek pathways that display significant differences (e.g., via ANOVA) or help predict outcomes (e.g., predictive modeling).
This new release also allows you to integrate pathway membership information from new sources. We have added support for incorporation of pathway information from partner application Ingenuity Pathways Analysis. You can contact Ingenuity technical support to obtain the Ingenuity Canonical Pathway Membership data for import into JMP Genomics. This file contains pathway membership information that you can use in our new Get IPA Pathways process to incorporate Ingenuity pathway information directly into your data set for use in both Gene Set Enrichment and Gene Set Scoring. Our IPA Upload process has been in JMP Genomics for quite a few versions now and lets users of both Ingenuity and JMP Genomics upload interesting gene sets directly to their IPA accounts for pathway exploration. I know our mutual users will be glad to have an additional option to integrate IPA pathway membership information directly into enrichment tests in JMP Genomics 5.
You also asked us for a simple way to export a JMP Genomics data set and phenotype information for use in Broad’s GSEA tool, and in response, we added an Export to GSEA format utility. If you’d prefer to use MSigDB gene sets directly within JMP Genomics enrichment or gene set scoring processes, you can also visit the MSigDB website to download gmt files and then add this information to your data set using the Merge MSigDB Gene Sets utility.
A new tool that is complementary to our new and enhanced enrichment tools is the Venn diagram. JMP Genomics has featured Venns since Version 3, prompting at least one case where a competitor with inferior Venns revamped its platform with requirements directly influenced by our features. You know who you are, and with this release, we take our revenge. (Sorry, I couldn’t resist.)
We’ve added a new Multiple Table Venn Diagram process that you can use to display the overlap in up to five gene lists by specifying a common identifier variable for each list. This complements our existing Venn diagram implementation, which uses a single list and up to five columns of binary group membership variables (such as the significance indicators we create automatically in ANOVA output) to determine overlap of list members.
In both Venn platforms, sectors are clickable for easy retrieval of interesting gene sets with the subset function in JMP. You can modify labels and move labels, change colors, and select proportional area Venns for up to three groups. Venn diagrams are one feature that our JMP life sciences products have that aren’t in JMP. We’ve created them with JMP scripts that are maintained and supported by our life sciences group. In addition to being quite beautiful, they’re a super complement to JMP’s powerful row selection tools, including the JMP Data Filter.
One final related tool that I wanted to mention here is the Create Annotation Analysis Group Variable utility. Found under the Genetics Utilities section, this process can be used to add a new grouping column to a SNP annotation data set to allow group-level analysis. You may group features by genomic location using bins of a specified width or number of markers, or specify a separate gene data set with start and stop information to group features by gene, with optional inclusion of upstream and downstream regions. You could use the new grouping variable for analyzing a group of SNPs together in our new Multiple SNP-Trait Association or for summarizing p-values in our new P-Value Combination process.
All in all, there are many new features to love in JMP Genomics 5 when it comes to incorporating pathway and annotation information!