7. Set the dropdown values of Size of functional category to 5 (‘min’) and 350 (‘max’). Large pathways are of limited interpretative value, while numerous small pathways decrease the statistical power because of excessive multiple testing.
8. Set the dropdown Size of query/term intersection to 3. The analysis will only consider more reliable pathways that have three or more genes in the input gene list.
9. Click g:Profile! to run the analysis.
   • A graphical heat map image will be shown with detected pathways shown along the y axis (left) and associated genes of the input list shown along the x axis (top). Resulting pathways are organized hierarchically into related groups.
   • g:Profiler returns only statistically significant pathways with p-values adjusted for multiple testing correction (called q-values).
   • By default, results with q-values below 0.05 are reported.

1. macOS for the first time,
   • error “gsea.jnlp cannot be opened because it is from an unidentified developer”.
   • Click on “Ok”.
   • Instead of double clicking on the gsea.jnlp icon/file, right click and select “open”.
   • The same error will appear but this time it will give you the option to “Open” or “Cancel”. Click on “Open”.
   • After this initial opening, subsequent double clicks on gsea.jnlp will launch GSEA without errors or warnings.
2. If GSEA still fails to launch through the Java Web Start downloaded from the GSEA website,
   • GSEA can alternatively be launched from the command line.
   • Go to the GSEA download site and download javaGSEA JAR file.
   • Open a command line terminal.
   • navigate to the directory where the file javaGSEA.jar was downloaded.
   • Run the command "java –Xmx4G –jar gsea-3.0.jar".

TROUBLESHOOTING: GSEA may seem non-responsive but it is actually loading in large files.
• Possible reason:Large GMT file
  • It may take 5-10 seconds for GSEA to load input files. The files are loaded successfully once a message appears on the screen “Files loaded successfully: 2/2. There were no errors”.

TROUBLESHOOTING: How to use MSigdb or alternate GMT files.
• GSEA also supplies its own gene set files that are accessible directly through the GSEA interface from the MSigDB resource.
• These files do not need to be imported into GSEA. When you define the GMT file, the MSigDB gene set files can be found in the first tab “Gene Matrix (from website)” of the “Select one or more genesets” dialog. The latest versions of the MSigDB gene set files are in bold but previous versions can also be accessed. To select multiple gene set files, use multi-file select by simultaneously clicking on the desired files and holding the control key on Windows or command on macOS.

Specify the following parameters::

6. Gene sets database –
   • click on the button (…) located to the right and wait a few seconds for the gene set selection window to appear.

• Go to the Gene matrix (local gmx/gmt) tab using the top right arrow.
• Click on the downloaded local GMT file Supplementary_Table3_Human_GOBP_AllPathways_no_GO_iea_July_01_2017_symbol.gmt(click for info on data files) and click on OK at the bottom of the window.
• 7. Number of permutations – number of times that the gene sets will be randomized to create the null distribution to calculate the p-value and FDR q-value. Use the default value of 1000 permutations.
  8. Ranked List – select the ranked gene list by clicking on the right-most arrows and highlighting the rank file(Supplementary_Table2_MesenvsImmuno_RNASeq_ranks.rnk)(click for info on data files).
  9. Click the Show button next to Basic fields to display extra options.

TROUBLESHOOTING: Increasing the number of permutations will increase computation time.
• The higher the number of permutations, the longer the analysis will take.
• To calculate the FDR q-value for each gene set, the data is randomized by permuting the genes in each gene set and recalculating the p-values for the randomized set. This parameter specifies how many times this randomization is done.
• The more randomizations are performed, the more precise the FDR q-value estimation will be (to a limit, as eventually the FDR q-value will stabilize at the actual value). On a Windows machine with 16G of RAM and i7 3.4 GHz processor, an analysis with 10, 100, 500, or 1000 randomizations on our example set with above defined parameters takes 155, 224, 544, and 1012 seconds, respectively.

TROUBLESHOOTING: GSEA looks non-responsive but it is actually computing enrichments.
• Possible reason: No progress bar.
  • GSEA has no progress bar to indicate estimated time to completion.
  • A run can take a few minutes or hours depending on your data size and computer speed.
  • Click on the “+” in the bottom left corner of the screen to see messages such as “shuffleGeneSet for GeneSet 4661/4715 nperm: 1000”
  • This message indicates that GSEA is shuffling 4,715 gene sets 1,000 times each, 4,661 of which are complete. Once the permutations are complete, GSEA generates the report.

TROUBLESHOOTING: Error message "Java Heap space".
• Possible reason: GSEA was launched with insufficient memory.
  • The error message “Java Heap space” indicates that the software has run out of memory.
  • Another version of GSEA is needed if you are running the Web Start application. There are multiple options available for download from the GSEA website. You can download a webstart application that launches GSEA with 1, 2, 4, or 8GB. Upgrade to a webstart that launches with more memory.
  • If you are already using the webstart that launches with 8GB then you require GSEA JAVA jar file which can be executed from the command line with increased memory .

Examine GSEA Results:

14. Once the GSEA analysis is complete, a green notification “Success” will appear in the bottom left section of the screen.
    • All GSEA output files will be automatically saved and available in the folder you specified in the GseaPreranked interface (Specified in Step 6Bxii).
    • Click on “Success” to open the results in your web browser.
    • Pathways enriched in top-ranking genes (i.e. up-regulated) are shown in the first set (‘na_pos’; ‘mesenchymal’ in this protocol)
    • Pathways enriched in bottom-ranked genes (i.e. down-regulated) in the second set (‘na_neg’; ‘immunoreactive’)

Examine GSEA Results- cont'd:


15. In the web browser results summary, click on the “Snapshot” link under the results to get an overview of the top 20 findings.
    • The most significant pathways for the first phenotype (‘na_pos’) should clearly display enrichment in top-ranking (i.e. up-regulated) genes (left side of the plot.
    • the most significant pathways for the second phenotype (‘na_neg’) should clearly display enrichment in bottom-ranked (i.e. down-regulated) genes (right side of the plot)

TROUBLESHOOTING: How to access previous results.
• Possible reason: GSEA application was closed since running the analysis but you would like to see previous results.
  • If the GSEA software is closed, you can access previous results by opening the working folder and opening the ‘index.html’ file.
  • Alternatively, you can re-launch GSEA and click on “Analysis history”, then “History” and then navigate to date of your analysis.
  • Although all analyses, regardless of where the results files were saved, are listed under history, it is organized by date the analysis was run. If you cannot remember when you ran a specific analysis, then you may have to manually search through a few directories to find the desired analysis.

TROUBLESHOOTING: What will GSEA use for phenotype labels in a CLS file that specifies more than two phenotypes?
• When running GSEA with expression data as input (instead of a pre-calculated rank file), a phenotype label (i.e. biological condition or sample class) is provided as input for each sample and specified in the GSEA ‘cls’ file.
• When running GSEA, the two phenotypes to compare for differential gene expression analysis are specified and these phenotypes are used in the pathway enrichment result files.
• In contrast, in a GSEA preranked analysis (i.e. when a ranked gene list is provided by the user), GSEA automatically labels one phenotype “na_pos” (corresponding to enrichment in the genes at the top of the ranked list, where ‘na’ means the phenotype label is "not available") and the other “na_neg” (corresponding to enrichment in the genes at the bottom of the ranked list). This convention is also used by the EnrichmentMap software, designating the first phenotype as “positive” and the second phenotype as “negative”.

TROUBLESHOOTING: Few or no results returned by GSEA
• Possible reason: Possible identifier mapping issue.
  • Check the number of gene sets that were analyzed. If the number is low (e.g. low hundreds), it could indicate gene ID mapping problems.

TROUBLESHOOTING: Autoload of g:Profiler results creates many datasets with incorrect file specification.
• Possible reason: There are too many text files within the directory specified.
  • To simplify loading g:Profiler results into EnrichmentMap and populating the correct fields in the EnrichmentMap interface, place the g:Profiler results file and gene set file (i.e. Supplementary_Table4_gprofiler_results.txt and Supplementary_Table5_hsapiens.pathways.NAME.gmt) into a directory together by themselves.

4. • In the right-hand pane, modify Name of the created data set if desired. By default, EnrichmentMap will use the name of the g:Profiler enrichment results file (e.g. Supplementary_Table4_gprofiler_results.txt).
5. • Verify the Analysis Type is set to “Generic/gProfiler”.
6. • Verify the Enrichments results file is the g:Profiler file downloaded in Step 6Axi (or alternatively, manually specify Supplementary_Table4_gprofiler_results.txt).

7. • Verify the GMT specified is the file retrieved from the g:Profiler website in Step 6Axii . Use the file hsapiens.pathways.NAME.gmt (or alternately manually specify Supplementary_Table5_hsapiens.pathways.NAME.gmt) that contains gene sets corresponding to GO biological processes and Reactome pathways.

8. • Expressions - (Optional) Upload an expression matrix for the genes analyzed in g:Profiler or alternatively an expression data set of all genes. If the expression data set contains additional genes not used for the g:Profiler search, their expression values will still appear in the heat map of the enrichment map (for example file see Supplementary_Table6_TCGA_OV_RNAseq_expression.txt).

9. • Ranks – (Optional) Ranks for the gene list or the expression data can be specified (for example, see Supplementary_Table2_MesenvsImmuno_RNASeq_ranks.rnk).
10. • Classes – (Optional) GSEA CLS file defining the phenotype (i.e. biological conditions) of each sample in the expression file, for example, see Supplementary_Table7_TCGA_OV_RNAseq_classes.cls. ).
11. • Phenotypes – (Optional) If there are two different phenotypes in the expression data, update the phenotype labels so that ‘positive’ represents the phenotype associated with positive values (Mesenchymal in this example) and ‘negative’ with negative values (Immunoreactive in this example).

TROUBLESHOOTING: How to create g:Profiler map with more than one phenotype.
• Although an individual g:Profiler analysis only has one phenotype, it is possible to modify a single results file to contain two analyses.
  • This is relevant when the phenotypes are mutually exclusive.
  • For the analysis you want to associate with the additional phenotype (which would correspond to down-regulated genes in GSEA PreRanked, thus called “negative”) open the g:Profiler results file (preferably in a spreadsheet so you can easily modify a single column).
  • The fifth column specifies the phenotype. Update the column to have the value of “-1” for each result in the file.
  • Open the second analysis file. Copy all the results from the second file and paste them into the updated negative g:Profiler file.
  • Save the file and use it as the g:Profiler enrichment results file in the EnrichmentMap interface instead of the original results files.
  • Pathways corresponding to two phenotypes will be colored red and blue in the resulting enrichment map. One limitation with this approach is that a pathway cannot be included in both positive and negative sets.

TROUBLESHOOTING: What happens when you move the similarity slider bar?
• Moving the slider to the left (or right) will adjust the underlying similarity statistic threshold to make the resulting network sparser (or denser).
• The slider is set with predefined defaults but users can fine-tune the similarity metric by selecting "Show advanced options" at the bottom of the "Create Enrichment Map" panel.
• Predefined values appear as tick marks on the slider and include Jaccard > 0.35, Jaccard > 0.25, combined > 0.375, overlap > 0.5, overlap > 0.25.

14. Click the Build button at the bottom of the EnrichmentMap Input panel. A Building EnrichmentMap box appears and indicates the progress status. This box will disappear once the map has been created successfully.

TROUBLESHOOTING: How to easily create GSEA map with more than one dataset.
• If you specify a directory that contains multiple GSEA results rather than an individual GSEA results folder, EnrichmentMap will treat every GSEA results folder as its own data set. This enables easy multi-data set analyses. If you only want one data set but inadvertently selected the directory containing multiple GSEA results instead of selecting an individual folder, simply select the data sets you do not want to use and click on the trash can at the top of the EnrichmentMap input panel to remove them.

4. • In the right-hand pane, modify Name of the created data set if desired. By default, EM will use the first part of the GSEA folder name prior to the last dot ‘.’ to create the data set name. For example, if the directory is called Mesen_vs_Immuno.GseaPreranked.12345, the name will be populated as Mesen_vs_Immuno.GseaPreranked.
5. • Verify the Analysis Type is set to “GSEA”.

6. • Enrichment Pos - Verify that file is set to [your_path_to_gsea_dir]/Mesen_vs_Immuno.GseaPreranked.12345/gsea_report_for_na_pos_12345.xls where 12345 is a unique number generated by GSEA. Alternately navigate to the file Supplementary_Table8_gsea_report_for_na_pos.xls.
7. • Enrichment Neg - Verify that file is set to [your_path_to_gsea_dir]/Mesen_vs_Immuno.GseaPreranked.12345/gsea_report_for_na_neg_12345.xls where 12345 is a unique number generated by GSEA. Alternately navigate to the file Supplementary_Table9_gsea_report_for_na_neg.xls.
8. • Verify the GMT Verify that the file is set to Supplementary_Table3_Human_GOBP_AllPathways_no_GO_iea_July_01_2017_symbol.gmt.

TROUBLESHOOTING: What is the random number appended to the GSEA directory name?
• Every GSEA analysis generates a random number that is appended to the names of the files and directories. The number will be different for every new analysis.

TROUBLESHOOTING: Enrichment map uses a GMT file that was not the original file input to GSEA.
• If EnrichmentMap cannot find the original GMT file used in the GSEA analysis, it will use a filtered GMT file found in the GSEA ‘edb’ results directory. EnrichmentMap will not be able to find your original GMT file if you have moved it since running GSEA analysis. Although it is a GMT file, it has been filtered to contain only genes found in the expression file. If you use this filtered file, you will get different pathway connectivity depending on the expression data being used. You should always use the original GMT file used for the GSEA analysis and not the filtered one in the results directory.

9. • Ranks - (Optional) Verify that the file is set to ranked_gene_list_na_pos_versus_na_neg_12345.xls where 12345 is a unique number generated by GSEA. Alternately, navigate to the file Supplementary_Table2_MesenvsImmuno_RNASeq_ranks.rnk).
10. • Expressions - (Optional) Supplementary_Table6_TCGA_OV_RNAseq_expression.txt).

11. • Classes – (Optional) GSEA CLS file defining the phenotype (i.e. biological conditions) of each sample in the expression file, for example, see Supplementary_Table7_TCGA_OV_RNAseq_classes.cls).
12. • Phenotypes – (Optional) In the text boxes replace ‘na_pos’ with ‘Mesenchymal’ and ‘na_neg’ with ‘Immunoreactive’. ‘Mesenchymal’ will be associated with red nodes as it corresponds to the positive phenotype while ‘Immunoreactive’ will be labeled blue.

TROUBLESHOOTING: What is the random number appended to the GSEA directory name?
• Every GSEA analysis generates a random number that is appended to the names of the files and directories. The number will be different for every new analysis.

TROUBLESHOOTING: Cannot provide a more precise value.
• Scientific notation is not enabled. To set the threshold to a small number, select ‘Scientific Notation’ and set a q-value cutoff such as 1E-04.

TROUBLESHOOTING: What happens when you move the similarity slider bar?
• Moving the slider to the left (or right) will adjust the underlying similarity statistic threshold to make the resulting network sparser (or denser).
• The slider is set with predefined defaults but users can fine-tune the similarity metric by selecting "Show advanced options" at the bottom of the "Create Enrichment Map" panel.
• Predefined values appear as tick marks on the slider and include Jaccard > 0.35, Jaccard > 0.25, combined > 0.375, overlap > 0.5, overlap > 0.25.

15. Click the Build button at the bottom of the EnrichmentMap Input panel. A Building EnrichmentMap box appears and indicates the progress status. This box will disappear once the map has been created successfully.

TROUBLESHOOTING: Few or no pathways present in enrichment map.
• A pathway enrichment analysis resulting in few or no enriched pathways may be caused by suboptimal statistical processing used to define the original gene list.
• Enriched pathways are unlikely to be found if the gene list ranks are too noisy and the most important genes are not at the top of the list, no genes are highly significant, or a large fraction of genes are highly significant.
• If the gene list has been correctly defined, analyzing further databases of pathways and gene sets or setting more liberal filters may improve results.

TROUBLESHOOTING: Cannot find any pathways with my search gene.
• Check that the gene identifier type used for the search matches the identifier type used in the analysis.
• Multiple genes separated by spaces can be entered into the search bar. Any pathway that contains the gene will be selected and highlighted in the network.
• Adding keywords “AND” into the search bar will show only pathways that contain all genes in the search query (e.g. "geneA AND geneB").
• If the original analysis did not use gene symbols then you will not be able to search by gene symbols. Instead use the identifier type that the analysis was based on, for example Entrez Gene ID or Ensembl gene ID.

TROUBLESHOOTING: Very few entries in node table although the network contains many nodes.
• Some nodes in the network are already selected.
• If there are very few records in the node table make sure that no nodes are selected in the network. Or click on the gear icon and change the setting from “Auto” to “Show all”.

TROUBLESHOOTING: An expression matrix was not supplied.
• If no expression file is input to EnrichmentMap it will automatically create a dummy expression file where any gene found in the enrichment file will be given a placeholder expression value of 0.25, and any gene found in a pathway but not found in the enrichment results file be assigned a placeholder expression value of NA. Therefore clicking on any node in the enrichment map will show the genes used for the analysis as well as genes in the pathway that are not part of the query set.

2. Define Genes you wish to visualize in the heat map (section Bb). Data can be viewed for all genes contained in the selected nodes (union of nodes) or just for the genes common to selected nodes (intersection of nodes). By default, all genes are shown.
3. Change Expressions value visualization depending on your data type (section C). Data can be viewed as it was loaded (Values), or row normalized where the row mean is subtracted from every value and then divided by the row’s standard deviation (Row Norm), or log transformed (Log).

4. Compress heat map columns (section 8D). By default, all expression values are visible as individual columns in the heat map for expression sets with less than 50 samples. It is possible to compress the data in a single column by selecting Compress, Data Set using the aggregation methods Median, Max (maximum) or Min (minimum). If a CLS file has been uploaded, the expression set can be compressed using one column per defined sample group using the Class option. If the expression matrix contains 50 samples or more, EnrichmentMap will automatically compress the values to their median value by default.
5. Check Values (section E) to show the expression numerical values in addition to the heat map color scale.
6. Additional fine tuning of the heat map can be done through the Settings Panel (section F) that includes functionality to add new rank files, export the heat map data as a tab delimited text file or PDF image, change the distance metric for hierarchical clustering, or turn on the node table heat map autofocus.

TROUBLESHOOTING: Heatmap column names are not colored by dataset
• No CLS file loaded or mismatch between CLS file and phenotype definition.If the heat map columns are not colored for a GSEA analysis, make sure the phenotype names specified in the EnrichmentMap input panel match the class names specified in the class file (MesenchymalvsImmunoreactive_RNA-Seq_classes.cls). Also see TROUBLESHOOTING 9Bxiii.

TROUBLESHOOTING: Option to save only leading edge genes is not given.
• Selection includes more than one node or data set contains no leading edge information (i.e was not built from GSEA results)
• The leading edge is only available for GSEA analyses. The option will only appear if the Enrichment map was built with GSEA results and a rank file was specified.

TROUBLESHOOTING: Which is the best layout to use?
• There are many different layout algorithms available in Cytoscape that can be used for EnrichmentMap.
• We recommend using an edge weighted layout, which considers the overlap score between pathways.
• Most layouts (except yFiles) offer the ability to organize just the selected nodes.
• Experiment with different layouts to see which works best with your data. If you do not like the resulting layout, press command-Z on macOS or ctrl-Z on Windows or click on Edit --> Undo to revert to the previous view.

TROUBLESHOOTING: Labels contain uninformative words.
• Node names contain uninformative words that are not excluded by default or are not considered during network normalization.
• If particular non-informative words keep appearing in the labels generated by AutoAnnotate, try adjusting the WordCloud normalization factor.
• The significance of each word is calculated based on the number of occurrences in the given cluster of pathways.
• This causes frequent words such as “pathway” or “regulation” to be prominent. By increasing the normalization factor, we reduce the priority of such recurrent words in cluster labels. If that doesn’t help, you can add the non-informative words to the WordCloud word exclusion list.

TROUBLESHOOTING: Labels are bigger for bigger clusters but I want all the labels to be the same size.
• Setting to scale labels to the size of the cluster is enabled.
• The number of nodes in a cluster determines label size by default. Thus, the cluster size may relate to pathway popularity instead of importance in the experiment. Annotation labels can all be set to the same size by unchecking the option “Scale font by cluster size” in the AutoAnnotate results panel.

TROUBLESHOOTING: Pop up after selecting "Collapse all" shows up everytime I collapse my clusters.
• Have not specified "Don't ask me again" option on pop up.
• Once you click on “Collapse All” a pop-up window will show the message “Before collapsing clusters please go to the menu Edit->Preferences->Group preferences and select “Enable attribute aggregation”. There is no need to adjust this parameter repeatedly. Click on “Don’t ask me again” and “OK” if you have set this parameter previously.

TROUBLESHOOTING: Collapsing my network takes a long time.
• The larger the network or the more clusters in a network, the longer collapsing will take.
• For large networks, collapsing and expanding may take time. For a quick view of the collapsed network you can create a summary network by selecting the “Create summary Network…”. There are two options for the summary network: “clusters only” which creates a summary network with just the circled clusters, or “clusters and unclustered nodes” which creates a summary network that also includes the singleton nodes not part of any cluster.

TROUBLESHOOTING: Collapsed network contain grey nodes instead of colored as they were in the pre-collapsed network.
• Attribute aggregation in not enabled.
• If the nodes in the resulting collapsed network are grey then you forgot to enable attribute aggregation. Expand clusters and before collapsing clusters again, go to the menu Edit->Preferences->Group preferences and select “Enable attribute aggregation".

TROUBLESHOOTING: In the EnrichmentMap input panel, the bottom options "Publication Ready" and "Set Signature Edge Width" are not visible.
• The Node Layout Tool is open.
• Close the Node Layout Tools window using the x symbol located at the top right corner.

TROUBLESHOOTING: The created subnetwork is empty.
• Nodes were not selected prior to the creation of the subnetwork.
• Make sure that the nodes that will be part of the subnetwork are selected before creation of the subnetwork.

TROUBLESHOOTING: Exported image only contains a small subset of my network.
• Only what is visible in the view is exported.
• In image export, only the visible part of the map will be exported. Make sure that the entire network is visible on your screen before exporting.

TROUBLESHOOTING: The parameters shown do not match what I expected them to be.
• The creation parameters panel only shows the parameters that were used at network creation. If you modified the network using filters or the EnrichmentMap slider bars you will have to update the thresholds accordingly.