Submission process

Algorithm selection

In the first screen (Figure 18), you need to select the algorithm to use for the differential pathway analysis. The detailed parameters for each algorithm can be adapted by clicking the blue icon on the left of the algorithm’s box.

Adding data

After clicking “Continue”, you are presented with the now empty list of datasets. Click the “+ Add dataset” button to add a new dataset. Now, you have to select the type of dataset you want to load (Figure 20). To upload your own data, select one of the options under “Select a file from a local folder”. 

Tool testing

To test the tool, it is possible to quickly load example data. Currently, ReactomeGSA provides three example datasets, two (matched) datasets on melanoma associated B cells (proteomics and transcriptomics measurements) and one scRNA-seq dataset on B cells.

Finally, it is possible to directly load datasets from ExpressionAtlas. To do so, first navigate to ExpressionAtlas (in a separate tab or window) at https://www.ebi.ac.uk/gxa. 

Annotating experimental metadata

Once a dataset is added, you need to annotate the experimental metadata. This is necessary in order to define the groups for the differential expression analysis in the next step (Figure 21).

You can adapt the dataset’s name in the “Dataset name” box at the top. This name will be used for all results. To increase the readability, we suggest to use as short names as possible.

In case you load data from ExpressionAtlas or choose one of the example datasets (as shown in the Figure 21) the sample annotation table will already be pre-filled with certain metadata. In case you uploaded your own dataset, the table will only show the orange sample labels on the left.

To add an annotation, click the “plus” symbol on the right. This will add a new empty column to the table. First, add a heading to define the name of the property (for example, “treatment”). Next, add the values for every sample that you want to include in your comparisons. Samples without any values will simply be ignored.

Defining the experimental design

In the final step of adding a dataset, you have to define which groups to compare (Figure 22). The “comparison factor” drop-down menu contains all parameters that were annotated in the sample table before (if they contain at least two different values). “1st group” and “2nd group” define which groups of samples to compare against each other.

Depending on which “comparison factor” you select, the available values for the “1st group” and “2nd group” will change automatically.

Additionally, some gene set analysis methods allow you to define so-called “covariates”. These are parameters that might cause a bias in your result (i.e. the sequencing facility used) that you would like to correct for. Simply select the relevant ones for your experiment.

Once you click “Continue” you will be returned to the list of datasets where you will now see your annotated dataset in the list. If you want, you can add any number of datasets to a single request.

Starting the analysis

Once you click “Continue” from the dataset list view, the final analysis options are shown.

“Create REACTOME visualizations” is always selected. If it is de-selected, the result cannot be visualised in Reactome’s pathway browser. This option is generally only relevant to users of the ReactomeGSA R package.

If you select “Create reports” ReactomeGSA will automatically create a Microsoft Excel and PDF report of your results. Additionally, it will create a short R script that allows you to load your data directly into an R session.

In case you provide your email address, you are automatically notified as soon as the analysis is complete. The mail will contain direct links to the generated reports (if you chose to create them) and a link to the visualisation in the PathwayBrowser (Figure 23).

Launch the analysis by clicking the “GO” button.

If you provided an email address, you can also close your browser. The analysis will still continue on our servers and you will be notified as soon as it is done. Some analysis with many large datasets (for example comparing five TCGA datasets in a single analysis) may require up to an hour to complete.