One of the features of Pathway Analysis in GeneSpring is that experiment data associated with matching entities between the pathway and the chosen entity list is displayed in the form of Heatstrips next to the entities in the pathway viewer. By selecting an interpretation, Heatstrips will display the data according to the experimental conditions specified in the selected interpretation.

In GeneSpring 12.x, pathways that were imported or created in earlier versions of GeneSpring are referred to as Legacy pathways. This includes a set of 21 pathways that are packaged with your GeneSpring installation.

Yes, in Step 1 of 4 of the Single Experiment Analysis workflow, you can select what kinds of pathways (available for the chosen organism) are queried during Pathway Analysis depending on whether you would like to include only Curated pathways, or Literature Derived Networks, or both. You can even narrow down your selection based on the respective sources (WikiPathways, BioCyc, BioPAX, GPML, Hand created, and Legacy for Curated pathways; NLP and MeSH terms for Literature Derived Networks).

Yes, you can change the p-value cut-off after saving the results of the analysis in the form of a pathway list at the end of the Single Experiment workflow. Filters are available for both p-value and number of matching entities in a filter panel, which is located under pathway list panel in the left bottom corner of the Pathway View of an open pathway list.

GeneSpring uses a so-called “column mark” for annotations during custom technology creation. If column marks were inappropriately assigned at that time then you may encounter this issue. Please refer to section 3.2.2 Update Technology Annotations in the GeneSpring user manual for more information.
If the column marks were selected correctly and your pathway analysis still results in no matching pathways, please contact GeneSpring support at informatics_support [at] agilent [dot] com for assistance.

The Find Significant Pathways feature has been replaced with Single Experiment Analysis. Please refer to Q1. What is Single Experiment Analysis? for more details.

From GeneSpring 12.0 onwards, Single Experiment Analysis (SEA) replaces the Find Significant Pathways (FSP) workflow step available in earlier versions. Compared to FSP, SEA is enhanced with some of the features that were introduced to enable Multi-Omic Analysis (MOA). SEA identifies matching pathways for the entities of one experiment, compared to two experiments in an MOA.