HiSim Graph

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The HiSim Graph Tool

About the HiSim Graph Tool

The HiSim Graph, short for Hierarchical Similarity Graph, is a tool for grouping functional genomic datasets based on the genes they contain. For example: The user may want to determine what a set of experiments on alcohol preference have in common, and what makes various experiments unique from one another. Alternatively, one may wish to take a large set of studies of related phenomena and identify their shared or distinct substrates. In this situation one may want to know whether there is a shared biological basis for addiction and learning, and if so, what the substrate is. The user might also want to examine studies of a large number of related disorders and determine whether a more appropriate biologically-based classification can be constructed.
The HiSim Graph Tool is designed to address these goals; it presents a tree of hierarchical relationships for a set of input GeneSets. The structure is determined solely from the gene overlaps of every combination of GeneSets.

Understanding the Results of the HiSim Graph

It's best to use the HiSim Graph Tool with a knowledge on what set intersections are:
If GeneSet A contains Gene A, Gene B, and Gene C, and also GeneSet B contains Gene A, Gene B, and Gene D. Then the intersection of GeneSet A and GeneSet B will contain Gene A and Gene B, because an intersection of sets are whatever is contained in all sets intersected.
In terms of GeneSets, the smallest intersections (fewest GeneSets, most genes) are towards the right, and the largest intersections (most GeneSets, fewest genes) are on the left. When thinking about the genes in all the GeneSets, the roles are reversed (smallest number of genes on the left, largest number of genes on the right).

Figure 1: Relation of GeneSets to the HiSim Graph

HiSim Graphs must be interpreted in the context of the input GeneSets. The above example represents differentially expressed genes in multiple brain regions of alcohol preferring rats from a single study. The highest intersection represents a gene differentially expressed in all 5 brain regions. In this case, the highest intersection represents the highest amount of correspondence between data sets. As you move to the right, genes become more specific to the brain regions tested. Each solid node has children and can be collapsed by clicking on it. Leaf nodes are empty and colored by species, which is identified in a legend at the bottom of the screen.

Figure 2: A HiSIm Graph for diverse functions

If one were to start with multiple alcohol preference measures from different studies, the top of the HiSim Graph represents the correspondence between the experiments (such as well-characterized alcohol preference genes), and as you descend the graph the intersections describe more specific features shared between experiments (such as stress response or tissue source).

When starting with more loosely related inputs, interpretation becomes more difficult. If one started with alcohol preference, nicotine dependence, and traumatic brain injury data (Figure 2), the top of the HiSim Graph would represent more generic processes such as neural plasticity in this case.

Using the HiSim Graph Tool

Access the HiSim Graph Tool through the Analyze Genesets tab.
To generate a HiSim Graph, you must first select gene sets from a project. Projects may be created and updated by uploading GeneSets, searching the GeneWeaver database, or through the use of other tools in the GeneWeaver system. See the documentation for uploading GeneSets, Search, or Manage GeneSets to learn more about these functions. To select an entire project or multiple projects for analysis, check the box next to the project name. To select individual GeneSets within a project, click on the ‘+’ beside the project name and check individual GeneSets using the check boxes. Next, click on the HiSim Graph icon in the Analysis tools box to the left of the project list. Select the options you would like for the tool to run on, and click Run.
HiSimGraph AnalyzeGeneSets.png

Figure 3: Selecting gene sets and executing an analysis from the Analyze GeneSets page


Figure 4. The results page for the HiSim Graph. Most genes are connected to two of the input GeneSets. One gene is connected to three of the input sets. (Inset) The GeneSet Intersection page. GeneSet intersection data can be downloaded as a csv file for subsequent analyses. The GeneSets giving rise to each node can be stored in a separate project.

The HiSim Graph opens and the nodes can be selected to expand the graph. More details of each intersection can be viewed by clicking on the individual nodes in the tree. A link at the bottom of the frame allows download of the csv.

Figure 5. These options are available for the HiSim Graph, to change the way nodes interact with each other. The stats of the graph, as well as shortcuts and the legend identifying each species in the graph, are also displayed.


Figure 6. This shows the search function, which highlights paths between nodes containing the item searched for, whether it be gene, geneset, or species.


There are a number of options available to optimize the HiSim Graph analyses. You may access the following options on the Analyze GeneSets page by clicking on the blue '+' symbol to the left of the HiSim Graph Tool.


When the resulting HiSim Graph is unimaginably large, a bootstrapping filter is applied to reduce the output size. This step removes edges that are weakly supported by the underlying data, for example, those partitions of GeneSet subgroups that are driven by a single gene difference between the groups. If you would like the large, unfiltered graph instead, set this option to True to disable bootstrapping. Be warned this may stretch the graph's size.

Figure 6: A HiSim Graph with DisableBootstrap turned on (True).


Figure 7: A HiSim Graph with DisableBootstrap turned off (False).


Include homology to integrate multi-species data. This is done by using homologene mappings to relate identifiers across species. If homology is excluded, data from multiple species will be segregated into separate trees.
HiSimGraphHomology Excluded.png

Figure 8: Homology excluded. A separate map is drawn for mouse, no overlap with human is allowed.

HiSimGraphHomology Included.png

Figure 9: Homology included. GeneSets from mouse and human are allowed to be mixed and are intertwined as one.


The minimum number of genes for an intersection. The default of 1 means that all intersections will be displayed. Increasing the value means that intersections with fewer genes will not be displayed in the output, decreasing noise and displaying more robust correspondence between GeneSets. This generally has the effect of removing the topmost nodes.

Figure 10: As shown above, the left tree is with the default MinGenes = 1, the right tree is with the default MinGenes = 5.


The HiSim Graph can ultimately address questions among highly curated data such as how much dimension reduction does gene overlap provide. For example, one may take a large set of gene sets associated with mood disorders and ask whether the data are similar enough to group together, i.e., of all possible subset intersections, how many are populated, and is this result better than chance?
The maximum number of permutations to run is set to 0 by default since it can take a long time to run for large input sets. The genes contained in each GeneSet are permuted over the union of all genes in the input sets, controlling for the size of each GeneSet. The permutation tests measure the likelihood of getting a similar tree structure (Parsimony) or of getting a similar aggregation of genes in each intersection (Gene Aggregation). Note that this is a maximum value since the actual results may be fewer due to the time limit.
Parsimony is a simple measure of the percentage of observed intersections out of all possible intersections. This is mathematically defined as:
Phenome Map 13.png
Figure 11: For those that aren't aware of the mathematical implications of parsimony, think of it as one of the many measures of accuracy for a map. You want more parsimony, but you can't always get full parsimony.

Gene Aggregation is a measure of the total node/tree probability. Each node is scored based on the intersection of genes and gene sets. Then the product of these scores is used to assign an overall tree aggregation probability:
Phenome Map 14.png

Figure 12: Aggregation is another measure of accuracy that balances with parsimony in this tool, neither are ever fully accurate alone, but together they are more fine-tuned.


The maximum amount of time to spend doing permutations. For example, if Permutations is set to 100,000 and this value is 5 minutes, the result with either have 100,000 permutations (if they finished within 5 minutes), or will be truncated to the number of permutations which were able to finish within 5 minutes. The more time you give to PermutationTimeLimit, the more accurate your results will be.
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